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Clay is the infrastructure GTM engineers build on. Start building for free.

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Attention → AI Agents for RevOps.

Clarify → The Autonomous CRM. Sell more, admin less.

Clearskies → The Context Layer for Revenue AI.

Nooks → All your outbound. One AI workspace.

Rox → Revenue Agents for the Global 2000.

Sumble → Buying signals from tech stacks, people data, & active projects.

Terret → The answer-to-action engine for revenue teams.

Hey y’all! 👋

Earlier this month, Clay shipped a public API and CLI. Something every GTM Engineer building at the cutting edge has been begging for for the last 6+ months. And the one thing almost everyone (myself included!) said they’d never be able to ship.

The consensus logic was simple: Clay’s stickiness lived in its UI. Open it up, and the moat drains. Adam Robinson polled “all of the smartest Clay pundits in the world” and they told him Clay would absolutely never do it (by the way, I told him the same thing when we talked on the phone last month).

They did it anyway (I was wrong), which makes Clay’s decision to go this direction all the more noteworthy. Today, I’m sharing my personal opinion of what it means for our space.

What Clay shipped

On July 8, Clay released an API, a CLI, and an Agent Plugin that bundles both (plus MCP and skills) into a one-line install for Claude Code, Codex, and Cursor. From Kareem’s announcement:

I found two things notable:

1. It’s available on every plan, including legacy self-serve. And work triggered through the API or CLI burns the same credits as work done in the product. No API tax.

2. The comments read like a pressure valve releasing. GTM engineers, growth leads, RevOps operators, and even CROs, who started building in Claude Code/Codex/Cursor 6+ months ago and have been switching to tools that work natively inside these coding agents.

   

Why everyone said they’d never do it

Back in April, I wrote out why I didn’t think Clay could (or would) release a public API without crushing their business (and even texted it to Adam). Clay’s moat had three layers.

Layer one: negotiated access+pricing to 150+ data providers (for more, see: aggregation theory). This allowed builders to work across multiple data providers in a single workflow/Clay table. And also enabled the—now ubiquitous—‘waterfall’ feature (which everyone is now copying).

Layer two: workflow lock-in. Your enrichment logic, your templates, your prompts, your team’s muscle memory, all living inside Clay’s environment. This second layer is the expensive to rebuild, which is the real switching cost.

Layer three: Their go-to-market motion. I think the most underdiscussed/underrated. They accidentally stumbled into it. Selling to agencies (“Claygencies”) who build content on their behalf, creating a magical, organic flywheel for Clay. I unpacked this topic back in January of 2025: Clay’s Moat (not a sponsored post).

A public API threatens both #1 and #2. Once an endpoint exists, someone builds an abstraction layer on top of it. Your workflow logic moves out of Clay’s system and into your own codebase, where it’s portable. Clay becomes the backend instead of the product.

But there was always a counterpoint: Clay charges per credit, not per seat, so the unit economics shouldn’t care about the delivery mechanism. However, an API doesn’t kill revenue directly. It kills stickiness, and lost stickiness kills revenue over time.

Adam Robinson heard the same thing that I outlined above:

All of the smartest Clay pundits in the world told me they’d absolutely never ever do it. What does THAT tell you about the state of the world?

My Take: You’re an idiot if you’re building UI for people.

…Leave it to Adam to make my point more crudely bluntly. :) (Hello if you’re reading this, Adam 👋)

Why they did it anyway

Demand. Again, Adam put it well:

This way of doing things is in such high demand that a $5B darling unicorn is willing to satisfy it, even though it’s very not good for the stickiness of their business.

Two things I’d add.

First, the users changed. The most technical (and often highest-spending) slice of Clay’s market now builds in Claude Code, Codex, and Cursor. I’ve seen this shift happen firsthand (myself included). GTM builders don’t want a UI. They/we want an endpoint. You can see examples of GTM teams building in Claude Code during an event back in April (which feels like a decade ago).

Second, Clay had every excuse to sit still. $100M+ ARR. 14,000 customers. A $5B valuation. Enterprise NRR above 200%. Clay shipped the feature its own moat argued against, in the fastest-shifting stretch software has ever been through. Most companies at that scale defend. Clay went on the offense. Adapting to the (quickly) shifting environment. Impressive to see.

This isn’t the UI dying, either. As my friend (and the person who first showed me Clay), Andreas Wernicke, said in the comments:

This and UI have to coexist.

It just stops being the only door in. Here’s an excerpt from a piece I published recently about building agents inside your CRM:

The pushback (and what it tells you)

This is just the start for Clay.

Matthew Quan (Product at Clay) responded to a common question in the announcement post:

Clay isn’t porting its UI to an API, feature by feature. They’re rebuilding the primitives around what agents do well: workflows as code, no row caps, and an agent that converts the old format into the new one.

For builders of GTM tech

An API, a CLI, and MCP support are no longer “nice-to-haves” in 2027. They’re “need-to-haves.” Your most technical users (RevOps, growth, GTM engineers, and increasingly even the SDR and sales leaders hacking in these tools) already build out of the terminal, and they won’t use products that can’t meet them there.

I understand that this isn’t just a simple feature. It’s a product decision, a pricing and packaging decision, an architecture decision, a positioning decision, and a go-to-market decision.

Building API/CLI/MCP-first forces the whole org to re-orient around it. And I realize it’s a big and scary decision. But if even Clay is doing it—and remember, this was supposed to kill their business model—you probably should too.

For buyers of GTM tech

When a vendor ships a real API, your workflow logic moves out of their UI and into your own repo, where it’s version-controlled and under your control (see: “AI sovereignty”). That vendor is choosing to win your renewal on capability every month instead of on sunk cost. Reward that choice with your budget, and be suspicious of the UI-only, seat-walled alternative.

Second, buying and building stopped being opposites. The old question was build vs. buy. The new motion is: buy the infrastructure (data, enrichment, execution, agents) and own the intelligence. Even if you’re building in-house tooling, and more teams are than ever, you should be buying infrastructure from Clay and other agent-native companies (like Rox, Sumble, Attention, Nooks, Clearskies, etc.).

Buyers used to ask “will my team actually log into this?” Now they’re asking “can my agents call this?” API, CLI, and MCP coverage moves from an afterthought to a first-order buying criterion, next to data quality and price.

Within 12 months, I think the vendors who treat the agent as a first-class user will win the most technical, highest-spending slice of the market—operators building in the terminal. And if you’re an operator in GTM, become someone comfortable in the terminal.

It’s unclear how it’ll unfold exactly (which makes this moment so exciting!). So, please reply to this email with what you think happens from here! I’d love to continue to evolve my thinking here. Regardless, it’s a blast to watch as the space shifts so fast, and we all figure out the new playbook together.

That’s it for today!

As always, thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Related posts:

• Inside “ChatGTM”: Cursor’s Internal Sales AI Used by their 400+ Sales Org (SDRs are booking 3x qualified meetings and AEs are shaving ramp by 50%+. What Cursor’s Head of Enterprise Growth, George Hou, built. And whether you should build your own version.)

• How to Think About Build vs. Buy in the AI Era (“Build Your Intelligence. Buy Your Infrastructure.” With Kyle Norton.)

• 9 Lessons From 11 Growth-Stage Companies That Built GTM Agents In-House (Vercel, Ramp, LangChain, ClickUp, Deel, Vanta & others: what they built, their tech stacks, and what we can learn from them)

• 54% of the Fastest-Growing B2B SaaS Companies have a GTM Engineer (Research Report by The Signal)

Subscribe now

Thanks for reading this hand-rolled, 100% organic, farm-to-table (human-written) newsletter.

Presented by:

Clay is the infrastructure GTM engineers build on. Start building for free.

& Supported by:

Attention → AI Agents for RevOps.

Clarify → The Autonomous CRM. Sell more, admin less.

Clearskies → The Context Layer for Revenue AI.

Nooks → All your outbound. One AI workspace.

Rox → Revenue Agents for the Global 2000.

Sumble → Buying signals from tech stacks, people data, & active projects.

Terret → The answer-to-action engine for revenue teams.

Hey y’all! 👋

If you’ve spent any time on LinkedIn this year, you’ve seen a post claiming “your sales team needs to put all your sales context in a GitHub repo!” Routing logic, field definitions, playbooks, all versioned, all readable by agents. But, often, the person making this claim is a one-man agency owner. So, I’ve been on the hunt to find scale companies running this playbook. Today, we explore one such company.

I first met Tejas, CEO of Hightouch, when we both lived in the Mission district in San Francisco; I was working at Zoom Video, and he had a new little startup and was trying to sell me (I tried to buy, but couldn’t get through procurement with my dinky title). It’s been a blast to see them absolutely ripping over the last 6 years. Hightouch crossed $100M ARR in April (they’ve grown more than 100% in each of the past two years) and raised a $150M Series D at a $2.75B valuation in March.

I recently sat down with a legend, Nikko Georgantonis, the Head of GTM AI & Systems at Hightouch. What they’ve built is one of the more sophisticated GTM systems I’ve seen. And this is not an agency guy who did a Claude Code project over a weekend. This is what AI adoption looks like inside a revenue org that’s scaling fast enough to have real stakes.

Here’s what we cover in today’s post:

• The two-pillar setup

• Pillar 1: Skills for every rep

• Pillar 2: The four-layer agent repo

• Why a repo instead of a tool

• What’s working for them right now

• What they tried that got pulled back

• RevOps becomes the agent wrangler

Alright, let’s get into it.

Nikko describes his job in two parts.

Part one is adoption. He’s going to QBRs and teaching the revenue org what skills are and why they matter. The deliverable is a library of packaged skills that reps actually use.

Part two is automation. His team has historically owned the system admin work of RevOps (data cleaning, lead list uploads, field hygiene, answering “can you update this account” requests in Slack). He’s handing that work to agents.

What caught my attention is how differently he architected each pillar. The rep-facing stuff is deliberately simple. The RevOps stuff is where the sophistication lives.

The two-pillar setup

↳ Pillar 1: Skills for every rep

The skills pillar isn’t overly complex. The highest-usage skills are things like account research. What makes them work is how prescriptive they are. Every skill follows the same formula: Here’s where to look, what to do, what good looks like, what you shouldn’t do.

A few things stood out to me about how he runs this:

Skills are overlaid onto the customer engagement process. He’s not building random one-off prompts. Each skill maps to a stage of how Hightouch engages customers, and it covers the whole revenue org: AEs, SEs, post-sales.

Reps choose their tool. Skills run in Claude or Glean, whichever the rep prefers. Nikko manages both. Either way, the skill taps into Hightouch’s internal brain through Glean’s MCP, which gives it access to Gong calls and internal docs.

Forking is the point. He ships the prescriptive version, and reps fork it to fit their own workflow.

The BI unlock for leaders. Skills can call Omni (their BI tool) to pull live metrics. That opens up manager and CRO use cases: scheduled skills that send weekly pacing reports without anyone asking for them.

↳ Pillar 2: The four-layer agent repo

This is the part Nikko was most stoked about.

All of the logic for his RevOps agents lives in a GitHub repo. Execution happens in n8n. The repo has four layers:

1. Context. The documentation that used to live in Notion, made machine-readable.

Their customer engagement process. Lead routing logic. How territories get cut. Forecasting definitions. Every opportunity and account field, audited and defined. Any agent can reference any of it.

2. Coordination. The routing layer.

Example: Hightouch has an internal RevOps Slack channel where people ask for account updates, flag duplicate leads, request list uploads, etc. The number of possible request types is too large for one prompt. So the first agent is a router. It classifies the request and hands it to the right specialist agent, passing notes along with it. Longer-term, this layer is how lead routing and scoring move from rule-based to probabilistic.

3. Memory. Agent history.

The example that made this click for me: Nikko has an agent that sends sales managers a report on each AE’s milestones, how they’re pacing, and a recommendation for what to fix. When it runs again two weeks later, it reads the recommendation it gave last time and factors that in. Some memory belongs in the warehouse. Recommendation history belongs in the repo.

4. Prompts. The actual prompts.

Most people would let these live inside n8n. Nikko keeps a skeleton in n8n that calls the prompt files in the repo instead.

One important note: reps never touch the repo. They see the outputs (a Slack reply, a field in the CRM, a report, a human-review step in the sequencer/dialer). Skills are how reps build. The repo is how RevOps builds.

Why a repo instead of a tool

Notably, Nikko told me he can’t make tooling bets right now.

Is n8n the right workflow tool today? Yes. Will it be in six months? No idea. Eight months ago, his “skills” were Glean agents. Then the technology moved. He’s watched his own stack rotate underneath him fast enough that betting the logic layer on any single vendor feels reckless.

So the investment goes into the layer that survives tool churn. Context, coordination logic, memory, and prompts, written crisply enough that they can be connected to whatever executes next. When something better than n8n shows up, the migration is re-pointing an execution layer, not rebuilding a brain. This aligns with the thinking Kyle Norton shared on “owning your intelligence” (ie: you should not buy your gtm brain)—you want control, portability, and compounding advantage.

Because everything is documented in one repo, he can point an LLM at it and ask where the redundancy is, where logic is duplicated, and how he should restructure. The system becomes legible to the tools that maintain it. To me, this is the obvious/inevitable future. And why, even companies like Clay, are going API/CLI-first.

And the endgame, in Nikko’s words, is that his job just becomes Claude Code. n8n has an MCP now, so he can do the development in Claude Code and have the documentation write itself back to GitHub. Salesforce admin changes that document themselves. Agents checking the outputs of other agents. His team’s old admin backlog, from data cleaning to lead list uploads, handed off. He says they’re system is starting to compound.

(One aside for anyone who read our ChatGTM piece: Cursor skipped the repo entirely and fetches everything live. Different problem. ChatGTM serves reps operational data that goes stale in hours; Nikko’s repo holds business rules that should outlive any tool. Live data gets fetched. Business logic gets committed.)

What’s working for them right now

Territory planning on a digital maturity score. This one predates the agent repo and Nikko was almost dismissive of it (”it’s been around for a bit”), but it might be the most valuable thing he showed me.

He built a Clay workflow that enriches every account with a digital maturity score: does this company run a data warehouse, does it do omnichannel marketing, does it operate a loyalty program, plus other boolean attributes. “Financial services” is a broad industry, and the “digital maturity score” is the next layer down. This bespoke scoring system separates the Chimes of the world from a credit union down the street. And it’s how Hightouch cuts territories today.

The manager pacing reports. Covered above, but it’s a pattern you can steal ~pretty easily: scheduled skill, pulls defined metrics from BI, gives each manager a per-AE diagnosis and recommendation, remembers what it said last time.

Betting on AI-native vendors. One example: Hightouch is replacing a legacy sales engagement platform with Nooks’ sequencer. A Series D company swapping a legacy sales engagement platform for a smaller, AI-native vendor is atypical, but the reasoning maps to everything above: he wants tools with real MCP workflows built in, not APIs with MCP slapped in front of them. As Nikko told me, “It’s easy to tell the difference.”

What they tried that got pulled back

I asked Nikko what hasn’t worked (I find that knowing which potholes to avoid is sometimes more helpful than sharing an exact roadmap of what to build).

1. Black-box reports. Hightouch built agents for proactive analytics: check the dashboards, diagnose what changed, recommend what to do. That works. But, where it broke was mixing in net-new metrics that nobody had defined before. Users didn’t trust them. People are pessimistic toward AI output by default, and an undefined metric inside an AI-generated report reads as a black box. The lesson he took is that agents can diagnose metrics your team already trusts. They shouldn’t invent new ones.

2. The asset-building skill. The team at Hightouch shipped a skill that helps sellers build customer-facing assets, but he’s rolling it back (for now, at least). The failure mode is subtle. The skill does 40-50% of the work. Experienced sellers know exactly what bad looks like, so they finish the remaining half with no problem. But new hires can’t identify which half is theirs to do. So for the people who needed help most, the skill amplified slop instead of raising the floor.

3. Fully automated outbound. For enterprise sales, Nikko isn’t bullish on “automated outbound”, and Hightouch didn’t find success with it. Where automation does work for them is inbound: handling demo-request follow-ups. Nikko clarified this one:

100% automated outbound we are bearish on, but agent-assisted we are bullish (and why we bought Nooks).

RevOps becomes the agent wrangler

Nikko wrote in a recent post that the RevOps job is heading toward agent wrangling, and after seeing his setup, the phrase fits. Entire post below, I think it’s helpful to read:

Look at what’s actually left for his team once this system matures. The admin work is delegated to agents. The tribal knowledge is codified in the context layer. The reactive Slack-channel work gets triaged by a router. What remains is designing the system: deciding what context agents get, how they hand off, what they remember, and checking their work.

That’s a real answer to the question I keep chasing: who should own AI inside the GTM org?. At Cursor, it was a growth leader who built a product. At the 11 growth-stage companies I profiled, it was usually a GTM engineer, RevOps, or a technical founder. At Hightouch, it’s RevOps with engineering habits: version control, documentation, composability.

The tools will keep rotating. Nikko is planning for that instead of fighting it. Six months from now, the executor might not be n8n, and the retrieval layer might not be Glean, and for Hightouch, it won’t matter much, because none of the logic lives there.

RevOps is evolving. And I suspect the early-adopter hacker/tinkerer-type builders, like Nikko at Hightouch, will be the people running systems for AI-native GTM teams in the coming years. I’m excited to continue exploring the new playbook being built.

» » »

Thanks to Nikko for sharing the awesome stuff he’s building over at Hightouch (a terrific product/team).

Connect with Nikko Georgantonis on LinkedIn if you want to learn more from him (and he’s currently hiring a GTM Engineer!).

That’s it for today!

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Other posts:

• The head of enterprise growth at Cursor showed me the internal Sales AI he built (they call it “ChatGTM”) that’s helping their SDRs book 3x qualified meetings + decrease AE ramp by 50%+. It’s another example of an awesome in-house build.

• Best of The Signal (my favorite articles) → broken out into categories across AI GTM, GTM Engineering, Prospecting, GTM Tech, & Research Reports)

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The go-to-market industry has spent decades getting very good at one half of the job. We can tell you why you lost the deal. We can tell you which rep is sandbagging, which region is slipping, and which competitor keeps showing up in your closed-lost. What almost none of it does is change what happens on the next call. Exposing a problem is much easier than solving it.

Justin Shriber, Terret’s CEO, has been thinking about this problem space his whole career. He started at McKinsey, where he watched clients drop suitcases full of cash for root-cause analysis, only to watch the deck get put on a shelf to collect dust. Later, he ran large sales orgs at Oracle and LinkedIn and saw the same thing from the other side. Finding the answer is the easy part. Getting a rep in a different time zone, who only talks to HQ 2x/year, to change behavior on a live deal is the hard part.

What Terret is building aims to close this gap—between the right answer and someone acting on it.

And that’s why I’m excited to explore this topic (which is even more relevant in the AI era) in today’s sponsored deep dive on Terret.

Here’s what we cover in today’s post:

• Background: building “Cursor for sales”

• F1 analogy: the analyst, the pit crew, and the driver

• The closed-loop system (and why MEDDIC is dead)

• Build vs. buy: why governance, not effort, is the real argument

Alright, let’s get into it.

Building “Cursor for Sales”

Terret didn’t start here. The company began doing forecasting and conversation intelligence, the same category as Clari or Gong. Somewhere in that work, Justin realized the forecast wasn’t the valuable thing they’d built. The valuable thing was the platform underneath it. Because they were born in an agentic world, they’d built an AI-native system that pulled data from every revenue-facing source and used inference to join and normalize it, without a six-month warehouse project. Today, people call that a context graph or a revenue graph. Terret had one running years before the term got popular.

Once you have this, forecasting becomes just one of many use cases. The same loop runs on whatever question you point it at: why the forecast slipped, why pipeline is stalling in a segment, why one rep’s deals progress twice as fast as everyone else’s, why a product line isn’t landing. Terret is more generalizable than any single point solution. In a recent conversation, Justin told me:

I wanted to build “Cursor for sales.” Cursor doesn’t have one narrow kind of code that you write. And that’s what I was trying to create. Get to the right answer, but then implement it to the point where you actually have a working solution that is delivering a result or an outcome.

Cursor doesn’t lock you into one narrow kind of code. It sits a layer above. Similarly, Terret wants to sit a layer above forecasting, coaching, or competitive intel, to take revenue teams from answer to action.

Terret can stand up a demo on a customer’s own data in 48 hours using just CRM and conversation intelligence, run a POC with real results in two weeks, and fully deploy in a month. Their goal is to show a CRO something about their business that they didn’t already know within those first 48 hours.

F1 analogy: the analyst, the pit crew, and the driver

Justin shared a metaphor to explain how they differentiate in the market.

1. The McKinsey Analyst (”Ask”)

Imagine dropping a McKinsey consultant into your revenue org who has read every email, every document, every CRM update, and listened to every call, with perfect recall. That’s the revenue graph paired with a trained LLM. Ask it why win rates in EMEA dropped last quarter and it can actually answer, because it can pull the lost deals from CRM, the call transcripts from your CI tool, the email threads stuck in individual inboxes, and the product usage from wherever it lives, then normalize all of it. CI tools can’t do that alone. CRM can’t. And building your data warehouse to do it will take half a year and still stumble over unstructured data.

The hard part isn’t the question. It’s that sales has a deterministic half and a non-deterministic half. Art and science. The numbers are deterministic. The why behind them is not. LLMs are great at the why and bad at the numbers. Ask one how much you closed in North America last quarter and it’ll give you a different figure each time, then fold the second you push back. (We’ve all questioned our LLMs and heard them say: “You’re absolutely right! Sorry about that, it’s 12% MoM.”).

Terret’s forecasting legacy is what let them solve this. Forecasting is a quantitative problem, so they already had a deterministic approach to generating numbers. They give the model prescriptive instructions, then run a series of checks to confirm it computed what it was told to. The result is a system that gets the number right every time and explains the why behind it.

2. The F1 Pit Crew (”Operationalize”)

The analyst tells you what to do. The pit crew makes it real for everyone else. This is where playbooks, rep scripting, and the rest of the artifacts get built and pushed out at scale, with no human assembling slides. An answer (even a great one) is worthless if the “insight” just collects dust in an Enablement Playbook in Notion. The pit crew turns it into a motion the whole team can run.

3. The Driver (”Activation”)

The first two get you a great answer and a living playbook. Activation is making sure the artifacts actually get used, which is where most of this falls apart. Hand a rep a script and you have no idea whether they followed it. Terret watches rep behavior against the script, grades it, and feeds specific, evidenced coaching to both the rep and the manager so everyone moves the same direction.

This is the end of enablement as we’ve known it, and the start of what Justin calls “micro-enablement” moments. I ran ops and enablement for a 100+ BDR org at Zoom Video a few years ago, and the model was brutal even then. You’d launch a new product into a new market, run a training, and maybe half the team absorbed it. And the information would go stale a month later, buried in a portal nobody opened again. Micro-enablement flips this on its head. Instead of one stuffy session at SKO with hungover reps in an 8 a.m. ballroom, you get a script before the call, a 30-second grade after it, and a coaching point from your manager that same week. A rep gets more reinforcement on one playbook in a week than the old model delivered in a year, and every touch builds on the last.

Other things you can build when you have an analyst, pit crew, and driver in one continuous loop:

• Run a win analysis on EMEA over the last four quarters, and 3 minutes later you’ll have a root-cause analysis and competitor battle cards of how to overcome them.

• A living playbook built from your best reps actual language that your top closers use, not language a product marketer invented.

• Before a call, the agent scripts an opener pulled from top reps, anticipates the objections she’s likely to hit, and adapts to where the deal is and who’s in the room.

• After the call she gets a scorecard (she scored an 8 out of 10) with evidence of what she did well and what to fix.

• At the end of the week her manager gets a coaching sheet across every deal.

[A side note worth mentioning: Justin thinks the CRO role is about to change shape completely, and he frames it as a shift from Leonard Bernstein to Miles Davis. The old great CRO was Bernstein. A score, a rehearsed orchestra, everyone hitting their notes, and the team that played the written music best won. The market moves too fast for a score now. Miles Davis ran a loose framework and improvised as the music moved under him. “If you were Lenny Bernstein, one of the greats, your career is done,” Justin told me. “You can’t compete in sales like that anymore.” Harsh, but I think this is directionally correct.]

The closed-loop system (and why MEDDIC is dead)

Years ago, when I was running a company, one of my taglines was: “Actions > Insights”. The last decade of GTM was the decade of smarter/better/more “insights.” Endless Looker and Tableau dashboards, scorecards galore, and endless Salesforce reports. All of it sitting there waiting for a human to notice and act. My company wanted to automate the action instead of it sitting in a Tableau dashboard. When I said this to Justin (Actions > Insights), he (graciously) corrected me: “Insights plus actions is the way to build a revenue system today.” He’s right. Insights alone are shelfware. Actions without the insight behind them are guesses. You want both, wired together.

That wiring creates a closed loop. Terret’s agent sits on the calls. When a new objection comes up that isn’t in the playbook, it captures how other reps handled it, and by the end of the day, that response is in the bank. The system knows which questions got asked in deals that closed and which got asked in deals that died, and it feeds that back into the playbook automatically. The playbook isn’t a document someone updates quarterly. It’s a living, breathing thing that learns from what actually works.

Justin believes MEDDIC is dead too. It’s the qualification framework that came out of PTC in the mid-1990s, the one John McMahon is known for, built in part because PTC didn’t have a real product yet and needed rigor to compensate. It’s a great framework. It’s also 30 years old. Why run a static rubric from 1995 when the system can build you a qualification model from what your own closers actually do? Terret can fully automate MEDDIC if you want it, score against it, and suggest the next move. The better version is your own MEDDIC, derived from your top performers, not from a consultant’s template or an enablement manager’s best guess.

Build vs. buy: why governance, not effort, is the real argument

The obvious question from a CRO or a founder is, why not just build this myself with Claude and a few MCP connections? Justin shared what he’s seeing and why building in-house is not the right choice for most companies.

The first problem is governance. Your revenue data lives in systems with completely different access models. Email is user-based. CRM is hierarchical. If you MCP a model directly into each of them, you’re pulling data across those boundaries and trusting the LLM to decide what any given person is allowed to see. That’s a bad bet to make with customer data and competitive data. Terret builds the revenue graph with permissioning baked in, resolved against your CRM hierarchy, so the governance holds.

The second problem is cost. Ask a model to stitch together data from five systems on the fly and you’ve asked it to rebuild a small data warehouse on every single query. That’s how you burn tokens and end up with an unpredictable bill. Terret pre-packages the exact slice of data a given question needs, so the model gets a micro-payload instead of everything. That’s also why Terret can charge a fixed price instead of consumption-based pricing, which is the line that makes the CFO lean in.

The third is concentration risk. Build your stack on one model provider and you’re betting that provider wins. Terret runs a cocktail of models on the back end, swaps in whatever’s best as it ships, and manages that risk for you. You’re not married to one vendor’s roadmap.

None of this means building is always wrong. Stripe builds. Cursor builds. Ramp builds. But there are maybe twenty companies on earth with the engineering depth to build a true in-house solution for their revenue team (and keep maintaining it). For everyone else, building a context graph, a governance layer, and a multi-model router from scratch is a bad use of the next nine months. For reference, doing the same thing on something like Agentforce is a 6-9 month project with dedicated engineers on permanent integration duty. Terret needs your CRM, your email and calendar, and one RevOps person to keep an eye on it.

(Read more here: How to Think About Build vs. Buy in the AI Era | “Build Your Intelligence. Buy Your Infrastructure.” With Owner.com’s CRO, Kyle Norton. And Terret is a great example of infrastructure you should buy to build on top of.)

The takeaway

Terret’s main goal is to be more powerful, cheaper, and faster. Powerful because the analyst, the pit crew, and the driver run as one loop instead of three disconnected tools. Cheaper because of fixed pricing and one vendor instead of <several others> + a warehouse + an integration team. Faster because a playbook that used to take a product marketer six months now takes three minutes (and updates itself).

The answer was never the hardest part in sales. What was: getting the answer into a rep’s hands at the moment they need it, knowing whether they used it, and if it worked. Terret is the first thing I’ve looked at that treats that as the actual product instead of an afterthought. I’m excited to continue to watch what Justin and the team at Terret build in the space.

PS: Below is a 4-minute walkthrough of Terret that I recorded:

If you want to learn more about Terret, reach out to their team (and be sure to let them know The Signal sent you).

That’s it for today.

Thank you for your continued attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

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Terret → The answer-to-action engine for revenue teams.

Hey y’all! 👋

Wild week in gtm-tech land. HubSpot acquired Warmly and Zoom acquired Common Room. This comes just after the acquisition of Pocus by Apollo.io. I suspect consolidation continues to happen in the space.

All the while, more companies are deciding to build certain parts internally. Cursor’s Head of Growth, George Hou, gave us a look behind the curtain of what he built at Cursor (one of the wildest in-house builds I’ve seen).

This week, we’re graced with the presence of another goat of gtm, Kyle Norton. Kyle is the CRO at Owner.com, runs The Revenue Leadership Podcast/Newsletter, and (according to my calculations) is the most AI-pilled CRO in all of SaaS. He recently published a piece titled “Don’t buy your GTM brain” (worth reading in full, if you haven’t already).

The “Build vs. Buy” question is on every GTM leader's mind right now.

So, after reading Kyle’s piece on it, I caught up with him to go a layer deeper on this topic (and how it works at Owner.com). I also share my own experience from the last two years of co-building GTM engines/AI workflows alongside internal teams.

In this piece, we cover:

• How AI changed the calculus in favor of Build>Buy

• The pitfalls of building (it’s not all sunshine and rainbows)

• The risk of outsourcing (buying) your intelligence layer

• Why you can’t outsource your AI strategy

Alright, let’s get into it.

How AI changed the calculus in favor of Build>Buy

For most of the last decade, build vs buy wasn’t much of a debate. Building was slow, expensive, and, unless you had spare engineers and a very specific need, you bought the tool and moved on.

AI changed the calculus. Since the model jump in late 2025 (the 4.6 moment), building went from “expensive, slow, probably not worth it” to a real option. You can spin up a working prototype in an afternoon. Things you would have paid a vendor six figures for, you can now build yourself and customize to your precise needs.

I’ve personally seen this up close. For 18 months, I embedded inside teams to build+ship AI-powered GTM systems (things like: data orchestration, signal-based selling, scoring, routing, ICP mapping, automated outbound, and more). The results of these systems were often magical. I saw the possibility of AI to create real leverage.

The ROI ceiling (with AI) is higher than it used to be. Old sales automation got you a 10-25% bump. The right AI build can double+ a team’s output.

Here’s what Kyle said:

The old build vs buy conversation usually centered on cost, implementation time, customization, and whether engineering had the capacity. Those still matter, but they are incomplete for AI.

The better AI build vs buy filter has 2 categories.

First, infrastructure. These are the parts of the stack where reliability matters more than differentiation. Dialers, core CRM plumbing, enrichment pipes, call recording, data warehousing, identity, permissions, security, and the underlying models in most cases. Buy these unless you have an unusually strong reason not to.

Second, intelligence. These are the parts of the stack where your company’s unique judgment matters. ICP definitions, lead scoring logic, account prioritization, territory design, pre-call research, deal strategy, customer context, coaching philosophy, messaging, qualification standards, and the evaluation loops that determine whether AI output is actually good.

That second category is where I get much more cautious.

At Owner.com, the point is not to build AI for the sake of building AI. That’s AI performance theater. The point is to build systems that make our GTM motion better because they encode what we know about our customer, our market, our reps, and our operating model.

That is why the line I used at SaaStr matters: buy your infrastructure, build or own your intelligence.

“Own” is important because it does not always mean build every component from scratch. You can use vendors and still own the intelligence if you control the data, understand the logic, can export the memory, can evaluate the outputs, and can move the learning if you need to. You can also build something internally and still fail to own the intelligence if it is a fragile one-off workflow nobody understands.

Ownership is not about ego. It is about control, portability, and compounding advantage.

We’ll come back to this point.

The pitfalls of building (it’s not all sunshine and rainbows)

Building is easier now. But also messier. Here are some pitfalls to avoid:

Swimlanes.

Inside a hyper-growth company, it’s never one team executing a tidy little roadmap. At Owner.com, Kyle has three different groups shipping production AI: an applied AI function under BizOps and data, the product org, and RevOps. Kyle shared an example where one team built a tool, but it was incinerating API calls into the CRM, wasn’t compliant with another piece of their stack regulations, and broke part of the rep workflow somewhere else. They understood the adjacent motion well enough to build for it, and not well enough to avoid breaking it. Kyle’s response wasn’t “stop.” It was “build it with RevOps, dedicate me engineers who work only on GTM tech, and do the requirements and roadmap properly.” You can’t have one person YOLO a tool that breaks other things downstream.

You need an actual engineer.

Not a “GTM engineer” in the LinkedIn sense, which has come to mean a Clay-table operator building lists and a few automations. Kyle calls his function applied AI on purpose, to draw that exact line. Once something has to run in production every day, you need real software engineers. Kyle’s discipline is to push everything into GitHub so a human can inspect what exists, and to not run tools whose outputs you can’t see.

Budget for maintenance.

When I was consulting, we would build the engine and then “pass the keys” to the team. But we would have problems after we left if there wasn’t a person (or team) in-house to maintain (and build on) the engine we deployed. Because the systems were complex (and in some cases brittle), a CRO of a Series A company is not going to open the workflow builder to rewrite ICP logic when the market shifts, or debug a chain that broke because something upstream changed. That isn’t a knock on them. It’s not their job, and they shouldn’t have to reverse-engineer a system they didn’t build. But you have to budget for this reality.

Things decay, and some should be killed.

Kyle revisits internal builds around the 90-day mark and asks whether each one is still worth it. They’ve deprecated things. They’ve even killed things and then brought them back. One example: their lead-routing system worked well, then the person who built it got pulled onto other priorities, it degraded, reps drifted back to assigning leads by hand, and they had to rebuild it to win them back.

You need evals.

Evals are how you know the thing actually works. They are often the difference between a V1 that demos well and a production tool reps use every day. Most of the work in closing that gap is boring. When Owner.com built AI pre-call research, the applied AI lead flew to Toronto and sat beside the reps for a week. They would hand a rep a batch of leads, watch them work it, hear “this is weird” or “this is wrong, here’s why,” go fix it, hand them a fresh batch, run it again. The feedback in those sessions becomes the product. Builders focused on these feedback loops are indistinguishable from an early-stage founder building alongside their first customers.

Most teams treat the launch as the finish line. In reality, it’s the starting line. The real cost of “build” isn’t the build; it’s the standing capacity to keep the thing alive after it ships. If you can’t fund that capacity, you should buy.

The risk of outsourcing (buying) your intelligence layer

Buy your infrastructure.

The dialer, the CRM plumbing, enrichment pipes, call recording, the underlying models. The places where uptime, reliability, compliance, and scale matter more than uniqueness. There’s no prize for vibe-coding a mediocre dialer that goes down during a call blitz on a Tuesday morning on the last week of the quarter.

Build your intelligence (or at least own it).

ICP definitions, scoring logic, account prioritization, pre-call research, deal strategy, coaching philosophy, messaging, and the eval loops that decide whether the AI’s output is any good. The places where your company’s judgment is the actual product.

The reason this matters more with AI than it did with old software is that AI systems learn from the workflow and compound. The risk is rented learning. Here’s how Kyle explained it:

The risk is not that AI vendors are bad. Many of them are excellent. The risk is that GTM leaders treat every AI decision like normal SaaS procurement. The normal SaaS procurement process asks: “does this tool solve the problem?”

AI-native procurement also asks: what does this tool learn, where does that learning live, and who controls it?

The worst version is a black box that owns both the workflow and the memory. You get productivity, but the learning compounds somewhere else. The vendor gets smarter across your usage. Your company gets dependent.

Again, that might still be worth it in some cases. But it should be a conscious trade, not an accidental one.

“Own” does not mean “build from scratch.” You can use vendors and still own the intelligence if you control the data, understand the logic, can export the memory, can evaluate the outputs, and can move the learning when you need to. The inverse is equally dangerous. You can build something fully in-house and still fail to own it, if what you built is a fragile one-off that nobody left in the building understands.

Why you can’t outsource your AI strategy

The intelligence layer has to stay yours, and the capability to build and maintain it has to live inside your office walls (digitally). You can’t buy your brain from a vendor, and you can’t outsource your AI strategy either.

Which is why the most interesting thing in my conversation with Kyle wasn’t the framework. It was him. He’s a CRO who gets his hands dirty and thinks like a product leader, and that combination is what future GTM leaders will look like, more and more.

I asked him whether a CRO should know how to build a product roadmap. Two years ago, he would have said, no. Today, he said, “Yes, or at least someone senior on the revenue side has to.” His path into systems thinking came from Shopify, where if you couldn’t speak Product, you were treated as lesser than. He learned the discipline to be taken seriously there, and then realized the frameworks themselves were the unlock as a CRO (customer discovery with reps to build requirements, user stories, something close to Agile inside RevOps, etc.).

Kyle’s results of owning the GTM brain in-house speak for themselves. At Owner.com, an AE at ~$150k OTE brings in roughly $3M ARR. A 20x comp-to-close ratio compared to their competitors running door-to-door motions.

Looking forward

Kyle shared where he lands on the quickly evolving debate of Build vs. Buy:

I am bullish on AI vendors. I am also bullish on internal GTM engineering. The answer is not one or the other. The winning companies will be pragmatic. They will buy a lot of infrastructure. They will use best-in-class vendors where vendors are clearly better. They will move fast.

But they will be extremely deliberate about the intelligence layer. They will know what context matters, what memory should persist, what workflows create proprietary learning, and what parts of the system need to remain portable.

That is how AI maturity compounds. One good workflow becomes reusable context for the next workflow. One evaluation loop improves the next model output. One proprietary memory layer makes every future agent better.

The companies that win AI-native GTM will not have the most tools. They will not be the companies with the flashiest demos.

They will be the companies whose systems get smarter every week because they own the memory that matters.

Don’t build everything but don’t buy your brain.

Buy your infrastructure, build your intelligence. But don’t build a brain you can’t keep alive, either.

That’s it for today!

Thanks to Kyle Norton for the conversation. Go read his original piece, “Don’t buy your GTM brain,” over at The Revenue Leadership Podcast and subscribe to his newsletter + podcast.

And, as always, thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Related posts:

• Inside “ChatGTM”: Cursor’s Internal Sales AI Used by their 400+ Sales Org (SDRs are booking 3x qualified meetings and AEs are shaving ramp by 50%+. What Cursor’s Head of Enterprise Growth, George Hou, built. And whether you should build your own version.)

• 54% of the Fastest Growing B2B SaaS Companies have a GTM Engineer (Research Report by The Signal)

• 9 Lessons From 11 Growth-Stage Companies That Built GTM Agents In-House (Vercel, Ramp, LangChain, ClickUp, Deel, Vanta & others: what they built, their tech stacks, and what we can learn from them)

• Most Companies Are Using GTM Engineers for “Automated Outbound.” The Best GTM Teams Aren’t Stopping There. (21 real examples)

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Sumble → Buying signals from tech stacks, people data, & active projects.

Terret → The answer-to-action engine for revenue teams.

Hey y’all! 👋

George Hou (Head of Enterprise Growth at Cursor) and I caught up recently, and he (sort of sheepishly) mentioned an internal tool he had built for the sales team at Cursor. I (not so sheepishly) asked him to share more. Before I knew it, he was screen-sharing and saying things like “I can’t show you that part,” which, of course, made me all the more curious. :)

I’ve gotten to know George, and a few of the folks at Cursor, over the last year or so, and I see them as some of the most impressive people in go-to-market. Not just by resume, but by how they think from first principles, how they ship, and how little ego they carry. It’s a vortex of talent over there.

George is a legend, and a kind supporter of the work I’ve been doing with The Signal. So, he was down to give me (and y’all!) a peek behind the curtain of exactly what he built (thanks for sharing with us, George 🫡).

ChatGTM is gaining crazy adoption within Cursor’s 400+ person sales org, and it’s helping their SDRs book 3x more qualified meetings and decrease AE ramp time by 50%+.

Here’s what we cover in today’s article:

• What ChatGTM actually is

• Why they built it

• But why not just use Claude Cowork, ChatGPT, or Glean?

• The question on every Revenue Leader’s mind: Build vs. Buy

• Who owns AI x GTM initiatives inside Cursor

• The results across their 400-person sales org

• Three lessons from George

Alright, let’s get into it.

What ChatGTM actually is

Reps describe ChatGTM as AI that already knows their accounts and how they sell. The easiest way to understand is to watch it work, so here’s a day in the life.

• 7:30am, on the subway: their morning digest is waiting in Slack: context on today’s meetings and who’ll be in them, overnight news across their accounts, and key territory trends: usage spikes, fresh signups, new exec hires.

• 8:00am, at the desk: 25 outbound messages are already drafted, each tied to a signal and paired with a landing page personalized for the contact’s tech stack and role. The rep just reviews, tweaks, sends. The send is native to Gmail or Outreach, so there’s no copy-paste required.

• 8:45am, prepping for a call: the brief flags that the exec hates a hard pitch from an article she just published, so the rep walks in prepped on how peers approached her problems.

• 9:00am, on the call: the exec lobs a curveball about migrating off legacy infrastructure the rep has never heard of. A quick question to ChatGTM answers it live. They leave with next steps booked and the exec says she doesn’t need a POC.

• Between meetings: every call already has its recap email drafted and waiting, plus custom follow-ups to the threads that went quiet.

• 3:00pm, prepping tomorrow’s exec meeting: the rep builds the whole business case in one thread: the three why’s, the value drivers, a CFO-ready one-pager and deck. Then he maps the org chart to see who else needs to be in the room and how to reach them.

• 4:00pm: a Slack digest pulls everything worth catching up on: product releases, account updates, DMs. Where a release is relevant to an account, it drafts tailored outreach for them to review.

• Thursday, before the forecast: ChatGTM scans every deal, its notes, stage changes, and next steps, then assembles the forecast and drafts the weekly update for their manager and account team.

• Start of a new quarter, strategizing a new book: the rep kicks off ChatGTM on a research job that would’ve taken weeks: every intent signal, usage trend, past Gong call, and Salesforce record, plus each account’s key initiatives and tech stack. It comes back with a stack-ranked plan for how to spend the quarter.

^{The morning digest reps work from: pre-drafted messages, each with a landing page tailored to the contact’s stack, role, and seniority.}

The feed (UI) is the visible part. Underneath it, ChatGTM goes directly to the sources (eg: Salesforce, Gong, data warehouse, LinkedIn, third-party data vendors, and the open web) and pulls context on demand. This is a very unique approach (more on this later).

Roughly 70% of usage is outbound and account research. The other 30% is more varied (and interesting). One rep describes ChatGTM as her “fleet of agents.” She described it as a manager who coaches her before an external sales call or even an internal forecast meeting (“What are things that my manager will stress test me on?”).

It also has memory: individual preferences (”I like shorter emails,” “I care about these signals, not those”), and team-level context (like an org chart one rep corrects on a call that then propagates to everyone else).

Why they built it

George runs enterprise growth at Cursor. The team’s mandate is to convert intent signals into pipeline, at scale, across a GTM team of almost 500. So George did the unglamorous thing and sat next to the SDR team to watch their actual workflows.

He noticed that before a rep could pick a single account to work, they were opening seven tabs. A Hex dashboard, Salesforce, Gong, LinkedIn, and a couple of others. 45 minutes of aggregating data, and then they’d sit there staring at a blank canvas, trying to figure out how to reach out.

He told me his initial thought was that if he could make SDRs 20% more productive, that would be a win.

He started small. First, custom SQL queries to pull everything into one table. Then a scoring formula to prioritize who to hit. Then drafting the copy. Then, long-running agents to do deep account research. The agents were robust and comprehensive, but slow, expensive, and not dynamic. When George showed that long-running agent output to the SDRs, they found it very valuable, but wanted it to refresh every single day.

Around the same time, customer success and a few AEs saw it and started asking ad hoc questions (”give me the talk track that resonated with FinServ customers last quarter”). That’s when the chat interface was introduced. Chat, plugged into powerful tools and context, enabled anyone to ask anything. This design gave space for flexibility in how reps used it. In other words, he made the users of the product (reps)—who are subject matter experts—the builders of the product. (We’ll come back to this important point.)

^{Account research in action: a point of view, org chart build, the signals worth acting on, and a first pass at the three why’s.}

But why not just use Claude Cowork, ChatGPT, or Glean?

I get this question often. And Cursor actually had every possible tool on the shelf. So I asked George: why build your own?

Going from no AI to one of these tools is a huge leap. Of course it feels like magic. But it stalls fast.

These tools are genuinely good at small, simple pieces of the job: call notes, recap emails, call prep.

But to help do a rep’s job end to end, the AI has to know your business cold: your product, your pricing, who you sell to, how you sell, your whole GTM motion. Then it has to carry the work the whole way: build the org chart, pull the one line that matters from hours of Gong calls, shape a point of view and the three whys, draft the CFO one-pager and deck, tee up the outreach. That’s the hard part, and where these tools crack.

As one rep put it, using Claude meant repeatedly uploading data and re-explaining context.

Cursor isn’t alone here. The same questions come up with most sales leaders George talks to.

Can you point to an efficiency gain? Not whether reps like it and are using it. What number actually moved? One VP’s honest reflection after a few months: no real efficiency gain beyond better-written emails. If it didn’t inflect pipeline, ramp, or win rate, it’s an expensive assistant.

When’s the last time it was wrong? A RevOps leader admitted she still catches inaccurate outputs regularly. For every one you catch, how many does a rep miss and send to a customer anyway? Turning on an MCP isn’t the same as pulling the right data, and one wrong number can cost a deal.

Does it scale past your best rep? Your power user will thrive with anything. But “just build a skill” isn’t a plan. Will the other 90% find it, remember it, and use it? In an open chat box, most never get past the basics.

Where are the guardrails? A blank chat box has none. Nothing guarantees it pulls from the right data every time, or runs the way you actually sell: MEDDICC, command of the message, the three why’s. So the playbook you drill at SKO stays a slide deck. Whether it shows up in the work is left to each rep to remember.

And what does it cost once it’s actually used? It runs away from you. Ask for something like LinkedIn research and it uses your browser for 30 minutes, torching a chunk of the month’s budget in a single task.

None of this is a knock on these tools. They’re genuinely great, and Cursor’s reps still reach for them for a few use cases. But answering a question and doing a seller’s job end to end are different problems. George was trying to tackle the latter.

The question on every Revenue Leader’s mind: Build vs. Buy

Cursor isn’t the first company to build a full-blown sales platform. Ramp built a similar system in-house (which I covered in my piece: 11 growth-stage companies that built GTM agents in-house). But, George explicitly told me:

I don’t think most GTM teams should build this.

And I totally agree with his reasoning. Like Ramp, Cursor’s team is very AI-pilled; they have the internal resources (people and money), they have a ton of first-party data to use, the technical infrastructure (data warehouse and connectors) and data team in place, and have a ‘build over buy’ culture.

Plus, there’s a lot of context-engineering work underneath, which Cursor happens to be unusually good at (because building Cursor required exactly that). George shared with me that the core of ChatGTM took about two months. But that sat on top of a pile of earlier failures, compounded learnings, and hard-won knowledge about agents and data providers. None of that is just sitting on a shelf for an eager CRO or GTM Engineer at a Series D company.

Should small teams build a one-off agent in-house (lead qualification, call prep, an ICP rebuilt over a weekend)? Absolutely.

But what George built sits at the far end of the spectrum. It’s a full go-to-market product that reps work out of throughout their day.

They also, notably, didn’t rip out the systems of record. They kept Gong. They kept Salesforce. They kept Slack. They kept Nooks for the SDRs’ parallel dialing. ChatGTM sits on top of those and pulls from them on demand.

The “you need a GitHub repo full of sales context” idea you see all over LinkedIn? They didn’t do that either. In other words, all that context is not sitting in a data warehouse or in a GitHub repo. Instead, ChatGTM queries Salesforce, Gong, etc., in real time, going to the data warehouse or wherever the data already lives. It’s fetched on demand (via live API/MCP tool calls), not pre-loaded into a repo someone has to maintain.

Who owns AI x GTM initiatives inside Cursor

One of my biggest questions about this project is who actually built it, who owns it, and who maintains it?

It didn’t come from RevOps. It wasn’t handed to a recent GTM Engineer hire. It came from growth.

George started it solo. Wilson, a product research engineer who’s done ambitious harness engineering work (incl. building a search engine and browser!), built the infrastructure that finds and feeds the AI the right information at the right time. And Emily, a growth engineer, helped build key integrations. That’s the whole build team.

But the more interesting answer to “who owns it” is: the reps.

Cursor built ChatGTM in a way that enables a non-technical seller to create their own skills (repeatable workflows) and automations (a scheduled skill) in natural language, inside ChatGTM. They are the subject-matter experts. They shape how the product is used and evolves, and have created 500+ skills and 1,000+ automations

Sales reps are just piling in skills and automations, and every time I chat with them, they light up. They’re so excited to screen-share what they’ve built. Our sales team describes this as their ‘Cursor moment.’ The first time they got real superpowers from AI, because the thing actually knew their context and how they liked to work.

The results across their 400-person sales org

They started with a tiger team of 8 SDRs. That cohort is the most mature set of data/users (they have been using ChatGTM for ~1.5 months at the time I spoke with George).

Results (what we’re all here for):

The SDR team is booking triple the qualified meetings. The top 2 reps are at 4x.

Remember, the initial goal George was aiming for was to improve their outputs by 20%.

Separately, sales leaders are saying AE ramp time has decreased by more than 50%.

They’re tracking success against four core metrics:

1. Pipeline generated

2. Rep ramp time decreased

3. Sales cycles shortened

4. Time saved.

Anecdotes:

• Re: Pipeline generated — SDRs describe not being able to live without ChatGTM at this point. Some half-joke that if ChatGTM has an outage, they might as well go home because they can’t effectively outbound at the same pace. And to be clear: this team was AI-pilled before ChatGTM, with full-built skills that took months to build.

• Re: Ramp time — a strat rep used to need more than a week to get up to speed on a single account (Slack archaeology, Salesforce, Gong, piles of context tracked nowhere). Now they’re ramped on an account in 30-45 minutes. It’s day and night. Sales leaders are seeing the same thing. One watched a rep in their third week sound like a year-long veteran on a call.

• Re: Sales cycle — one rep described ChatGTM as her “fleet of agents” that collapsed multiple people into one chat: a manager who stress-tests her before a call, an SE who builds a usage graph, an SDR who sources contacts, an analyst who pulls closed-lost reasons. A sales leader had a similar moment on a call with a head of global AI, who fired off tough questions. Normally that’s a “let me get back to you.” Instead he answered live with ChatGTM five weeks in and booked the next step right then.

• Re: Time saved — one leader pulled out his phone at his son’s lacrosse practice, fired off a few requests to ChatGTM between plays, and had detailed research briefs and drafted email replies waiting by the time the game wrapped. He calls it having unlimited agents in his pocket.

The rate of adoption proves ChatGTM is valuable in the stack. The vast majority of the GTM org are DAUs.

When the SDR manager rebuilt the new-hire outbound curriculum, he told his team they were only allowed to use three tools: ChatGTM, Nooks (for parallel dialing), and LinkedIn. They should not be logging in to anything else.

Three lessons from George

I asked George what he’d tell someone who was at the beginning of this journey. He gave three concrete tips.

1. Work backwards from the metric you’re accountable for.

Find the fastest path to inflecting this number, whether that means buying or building on top. Shiny demos are cool until leaders ask for the impact. It’s hard to debate the cold metrics.

2. Nail one burning problem first.

Their approach early on was to throw agents at the problem and hope. That doesn’t work. An agent without the right context won’t magically solve anything. Most of the work is figuring out what context it actually needs, one workflow at a time.

3. Your team is the context.

Agents don’t come knowing how you sell. Your reps and leaders do. The real unlock is making it dead simple for those subject-matter experts to pour in their judgment, then getting out of their way.

Most teams won’t build their own version of ChatGTM, and George would be the first to tell you they shouldn’t. But, I hope this helps you think through “the art of what’s possible,” the ever-evolving (and nuanced) Build vs. Buy decision for revenue leaders right now, and how AI x GTM is shifting.

Oh, and if any Revenue Leaders are thinking through this now, George is happy to chat and share learnings. So reach out to him on LinkedIn.

Thanks again to George for giving us a peek behind one of the wildest in-house builds I’ve seen. We’ll have to revisit ChatGTM again in 6 months to get an update!

That’s it for today. Hope you enjoyed this insider peek into how one of the big 3 labs is building its GTM engine. Absolutely wild to see.

As always, thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

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Hey y’all! 👋

In May of last year, I argued that the future of CRM is autonomous. Then, in December, I followed up with The Autonomous CRM: 6 Months Later, after Clarify shipped Rep and AI Fields and the “autonomous CRM” moved from an idea for an investor pitch deck to core infrastructure that founders run their business on top of. Today, that story continues to evolve, as Clarify shipped its agent builder!

It lets you spin up autonomous agents directly inside your CRM. You tell it what you want in plain English, and under 5 seconds it’s built (literally; I know because I built one live—video later in this post, it’s magic). I think Clarify should be part of the conversation for any early-stage founder or GTM hire at a Seed-Series B company. I’m excited to walk you through everything in today’s sponsored deep dive on Clarify’s agent launch!

Here’s what we cover:

• The context arms race (and the layer you already own)

• Why the CRM failed us

• Agents fix the input problem

• From system of record to system of action

• I built an agent live (in 5 seconds)

• A starter list of agents to run inside your CRM

• Bring more data in, not less

• How SaaS UI evolves from here

• Looking forward

Alright, let’s get into it.

The context arms race (and the layer you already own)

You’ve heard it by now: your agents are only as smart as the context they can see. A sequencing agent that can’t see the prospect already replied “circle back in Q3” is worse than no agent at all. So the market has gotten obsessed with context, and a land grab is underway to be the context layer across GTM teams.

It has become commonplace to think the CRM is no longer the best place for context. But, in a world where agents are constantly working on your behalf to populate your CRM with context (eg: when every call, email thread, meeting, deal, stage change, contact is actually populated correctly)… I would argue the CRM can be the best place to house your context. The CRM was always supposed to be the brain. And now that you can keep it updated correctly, building agents on top of the place where the rest of your customer information lives is the obvious move.

Why the CRM failed us

If your CRM requires humans to update it, it will always be out of date.

And every GTM function downstream (forecasting, territory planning, attribution, scoring, and now every AI tool you bolt on top) inherits that staleness.

Everyone nods along that context should live in the CRM. The honest follow-up is that it usually doesn’t, because the data gets entered by reps at 8pm from the scribbles on their notepad (I lived this as a founder and AE), or it doesn’t get entered at all. Revenue slips through the cracks not because the system of record is poorly designed, but because nobody has the time to feed it. The container was right. The contents were garbage.

Agents fix the input problem

This is the unlock, and it challenges my thinking over the last few years around the CRM as the false ‘source of truth’.

When agents handle capture, the rep stops being the bottleneck. Calls get transcribed and summarized. Emails get parsed. Contacts get enriched. Stages move based on what actually happened in the activity, not what someone claims happened in a Friday pipe review. The system of record becomes self-maintaining.

The second-order effect is what’s interesting to me. Once the CRM is actually current, you can start building agents on top of it that are incredibly powerful. Most CRM automation has historically been mediocre because it was reasoning over stale, incomplete data. Garbage in, (consistently) wrong agent-output out. Fix the input, and everything downstream gets sharper at once.

That’s the case for running agents inside the CRM rather than in a tool floating above it. The capture loop and the action loop share the same source of truth, and that source of truth is finally trustworthy.

From system of record to system of action

In my piece last year, I described Clarify moving the CRM from a “system of record that takes from reps” to a “system of intelligence that gives them leverage.” The agent builder is the next step in that arc. The CRM becomes a system of action: a place where things happen, not just where you store what happened.

This is the same Assistant-versus-Executive distinction I keep coming back to. An Assistant waits for you to ask (”summarize this call”). An Executive acts on your behalf (”this deal went single-threaded after the champion stopped replying, I drafted a multi-thread email to the VP, want to send it?”). Agents inside the CRM are the most natural home for Executive behavior, because that’s where the full deal context already sits.

I built an agent live (in 5 seconds)

I recorded a Loom the first time I opened Clarify’s agent builder. No prep, no second take, just me describing an agent in plain English and watching it build.

I asked it to scan every call, pull action items tagged to each person, surface objections to follow up on, write the next step, and post it all to Slack. It built in about five seconds, named the agent, wired the trigger, wrote the instructions, and set tool access (read call recordings, write to Slack) on its own. It even added the meeting title, which I never asked for.

Here’s a real example of a Slack message the agent sent me:

This is what I said out loud in the video, and I still believe: my imagination is the limit now, not the tooling. The constraint used to be “can I build this.” Now it’s “what’s worth building.”

A starter list of agents to run inside your CRM

Here’s a tiered list to get the gears turning, from hygiene to autonomous action. Some of these are pre-built templates you can use in Clarify. Others, you can fork an existing agent or create one from scratch.

Tier 1: Capture and hygiene.

• Post-call summary to Slack with action items tagged per attendee, objections raised, and the next step. (The one I built live.)

• Stage updater that advances or flags a deal based on real activity, not on what a rep remembers to click.

• Competitor-mention tracker that scans calls and emails and keeps a “competitors in play” field current.

Tier 2: Proactive alerts.

• At-risk deal flag to Slack that watches for stalled momentum (no next meeting, champion gone quiet, single-threaded) and surfaces it before you check.

• Missing-persona alert that notices the economic buyer was never on a call and drafts the multi-threading email.

• Inbound ICP scorer that reads the company, scores fit, and routes accordingly.

Tier 3: Autonomous action.

• Signal-triggered outbound off a competitor outage, funding round, or relevant exec hire, drafted for you to approve.

• Win/loss post-mortem pushed to Slack and CRM written from the actual final calls and emails (AI Fields doing the analyst’s job).

• Lookalike sourcing that identifies the use case that closed a deal and goes finds more companies like it.

• Product feedback across entire GTM org. Every day, scan Sales/CSM call transcripts for any mentions of product bugs, feature requests, customer praise, etc. Check in linear to see if there are any matching tickets for a bug mentioned (if not, add one), add a Task in Linear for a PM about the feature request, check Stripe about their subscription, and notify their Account Manager in Slack.

Tier 1 makes the data trustworthy, Tier 2 turns it into nudges, Tier 3 turns nudges into actions. You can’t do Tier 3 well until Tier 1 is solved, which is the whole argument for why this belongs inside the CRM.

Bring more data in, not less

There’s a deeper unlock here that data and RevOps people will appreciate.

I felt this pain firsthand when I was building Groundswell, a company helping teams operationalize product-led sales. Getting first-party event/behavioral data out of the warehouse and into the systems where GTM teams live was brutal: reverse ETL, custom scoring models, PLG tools on top of the warehouse. The overhead was high enough that most companies didn’t bother, so the data stayed locked up where reps never saw it.

Agents change the calculus. An LLM can read raw activity and reason about what it means without it being pre-modeled, scored, and cleaned first. Stream the events in (product events, support tickets, first-party signals), let the agent reason over them, and act. So flip the old instinct: bring more data in, not less. The agent can use messy, raw, unmodeled data that would have been useless to a rule-based workflow. That lowers the bar for real automation for everyone, not just teams with a data engineer to spare.

How SaaS UI evolves from here

The cool, contrarian, new thing to say is that software is “going headless,” or that “UI doesn’t matter in a world of agents.” I think that’s a lazy take. I have a different prediction about how SaaS UI will evolve.

As agents get good, the GUI collapses from the console you operate all day into the console you open only to make decisions that carry real risk. The CRM is a great example. Historically, the GUI was the work, which is the source of the decades-old adoption problem: reps avoid the CRM because data entry is friction, so the record is always stale. With agents, capture stops being the rep’s job, and the (system of) record becomes self-maintaining.

What’s left for the human GUI is narrow and high-stakes. Approve the agent’s proposed stage change on a seven-figure opp. Override its territory routing. Inspect the branching history of everything it did to a strategic account before a QBR. Set the ICP and guardrails the fleet runs under. Step in on the relationship-sensitive moment (an exec escalation, an at-risk renewal). Plus, a read-mostly forecast you increasingly query in plain language (”why did Q3 slip?”) instead of clicking through.

So the rep’s relationship to the CRM moves from operating it to supervising a sales-agent fleet through a thin cockpit. They go from living in it all day to approving exceptions and reading the roll-up.

Two implications for anyone building or buying GTM software.

1. The adoption problem doesn’t get solved by better UI. It gets solved by taking the human out of the data-entry loop. The GUI doesn’t improve, it shrinks and moves up the stakes ladder.

2. Pricing decomposes rather than collapses. Humans still get seats (for access, and increasingly for governing the agent estate), and those seats may even multiply. But the agent’s work is usage-shaped, so it gets metered: actions, messages, resolutions, outcomes. As agents do more and the human-seat base shrinks, the revenue mix tilts from seats toward meters. The model that wins is seat plus meter, not one instead of the other. (This is the same shift I wrote about in The $100B+ Winner in GTM Tech Will Sell Labor, Not Software: budget moves from SaaS spend to labor spend.)

One bet sits under all of it. The GUI a human needs shrinks in direct proportion to how much they trust the agents. And the CRM is the first system where that gets obvious.

Looking forward

18 months ago Clarify had a vision. In December they had Rep. This week they have a way for you to build your own agents on top of the cleanest customer context you own. The trajectory has been consistent: take work off the rep’s plate, keep the record honest without anyone babysitting it, and let humans spend their time on the decisions that actually need one.

If you’ve ever lost a deal to a stale field, or watched your team drown in CRM admin, this is the version worth trying. Sign up for Clarify, connect your email, calendar, and Slack. And watch the CRM build itself, then build an agent and see how fast your imagination becomes the only constraint. It’s a magical new world we’re living in. Have fun!

That’s it for today. Thank you for your continued attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

PS: Patrick writes an awesome newsletter about “the honest reflections on the ups, downs, and in-betweens of startup and founder life,” called Founder Therapy.

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Happy Sunday!

During a consulting project in 2024, I was trying to find companies using PyTorch, TensorFlow (and other open-source ML frameworks), plus at least one full-time AI/ML engineer in-house. The technographics solutions I was using weren’t giving accurate or deep enough results. I found Sumble and haven’t looked back since.

As I’ve gone deeper with Sumble (and gotten to know the team* behind it), what’s most compelling to me isn’t their features; it’s the underlying data itself. Most vendors can tell you a 70,000-person enterprise “uses Grafana.” Sumble specifically tells you it’s the Platform Engineering team, what they’re building right now, and who leads it. (You can also use Sumble directly inside Claude or ChatGPT). Getting to that level inside a large, messy org is genuinely hard, and spending time with the team made clear why they can pull it off: they treat this as a machine learning problem first and a GTM product second.

*the founders (Anthony and Ben) previously founded Kaggle (in the AI/ML space), which was acquired by Google.

But the core focus of today’s piece is on their newly launched Account Scoring Skill, arguably one of the most important jobs to be done in go-to-market (it determines which accounts to focus on and how to engage them, which is the whole game). It’s something that’s been around for a long time, but I’ve always had beef with “scoring”, because salespeople never trusted these “scores.”

The pitch made sense on paper: score every account and contact, rank them, throw ‘em over the fence to your reps, and expect the list to get worked. 6sense and Demandbase grew into 9-figure businesses off that promise.

But, the old model was a black box. The new one is transparent (which matters even more as you deploy agents).

This shift and the downstream implications are massive. And the RevOps (and GTM) leaders building Account Scoring in this new way are getting results that are compounding across their GTM orgs. This is why I’m excited for today’s sponsored deep dive with Sumble.

Here’s what we cover:

• The black box problem

• The old way of account scoring, and what has changed

• The new rules of account scoring

• Score → reasoning (proof) → action

• Why data matters more for GTM teams using automation and agents

• Lessons learned from the field

• Where this goes next

Alright, let’s get into it.

The black box problem

Account scoring has been broken for a long time, for three reasons.

1. Opaque

2. Lack of data/context

3. Out of date (only gets updated ~1x/year)

I’m going to steal a section from a recent Sumble blog post, which is worth reading by the way (Account Scoring Should Explain, Not Just Rank):

“A score earns its place only if a rep can read it, trust it, and act on it. A good score doesn’t hand a rep a grade, it points them at their first move. Every number Sumble provides is backed by the people, teams, and projects behind it.”

We’ve all seen a CRM that has an account score of “92” or “A3” as a custom field. But what is a sales rep supposed to do with that? Add them to the “A3 sequence”? No. And worse, because the rep doesn’t know what it means, they don’t trust it, so it doesn’t get actioned, and the scoring model does not improve over time. I’ve seen this vicious cycle countless times.

Tl;dr: GTM teams have been building Account Scoring that sellers don’t understand/trust/use, running on data that can’t separate a great account from a big one, only refreshed once a year.

The old way of account scoring, and what has changed

The thing that actually changed is the data you can build a model on.

The old models ran on the data we had, things like industry, location, employee count, and if you’re feeling fancy, latest funding. Those are blunt data points. They put JPMorgan, Apple, and every other big logo at the top of every list. Sometimes that’s right. But more often than not, it isn’t. Thanks to AI (and magical tools like Sumble), we can get—what I call—”rich” data points. They call it “derived data”.

Not “how many employees.” But instead, things like: how many SDRs, and is that number growing, and how fast year over year. Not “what’s their tech stack,” but which three technologies appear in combination, which tells you something a single tool never could.

The cadence of updating an account score has also changed. It used to be a project you did during annual planning, in a spreadsheet, and then mostly left to collect dust in a Notion folder. By Q2 it was stale. That cadence is part of why “account fit” got treated as a separate, frozen thing from “is this account in market right now?” (which is what sellers actually need). When you only re-score annually, fit is the only thing you can really model, because timing changes faster than your once/year planning. But a model running on live data re-scores continuously. So when a relevant signal fires, the account moves up the list on its own. You don’t wait for the next annual review to notice that one of your accounts just posted 10 reqs for SDRs. That collapses the wall between fit and timing, because timing is now just another input the model watches in real time. (Spicy take, I know).

The new rules of account scoring

Anthony, Sumble’s CEO, has looked at 40+ different companies’ approaches to account scoring. I was catching up with him recently, and he told me the same mistakes show up again and again. Here’s the playbook he’s shared that the smartest RevOps are running:

Show rank, not score. What does a 92 actually mean? Nothing a rep can use. But “your fifth-best account” means something immediately. A raw score is an abstraction. What sellers really want is a ranked ‘call-down’ list (with context).

One model, segments applied after. A common mistake is building separate models for SMB, mid-market, and enterprise. Don’t. Run one model across everything, then slice the ranked output into segments afterward.

Score size by role, not by headcount. Total employee count rewards the wrong thing. Walmart has 2M employees, and you almost never want to hand Walmart points for that. If you sell to sales teams, the size variable that matters is how many SDRs and how many AEs they have, not how many total people work there.

Use concentration, not just absolute counts. If you sell dev tools, 100 DevOps engineers at a 400-person company is a far more interesting account than 100 DevOps engineers at a company with 100,000 employees. The ratio tells you how central your category is to that business.

Pro-tip: Anthony’s preferred decomposition is three buckets: size, growth, and concentration. Size handles the JPMorgans. Growth surfaces the fast climbers. Concentration tells you how much the account actually needs what you sell.

Keep funding out of the model. People love putting funding in. But, it breaks things. Funding only applies to venture-backed companies, so JPMorgan scores a zero on it and every Series B startup gets a bump. The result is a model that artificially inflates venture-backed accounts. And the useful part of “they raised a lot” already shows up in variables that touch the whole universe, like growth rate. So, score growth and drop funding.

Calibrate against your golden accounts, then trust the eye test. Feed the model your closed-won list, or an aspirational target list, and tune it so those known-good accounts rank where they should. Sumble’s Skill sets the initial weights (out of the box) and fits it towards that golden set, then humans adjust from there. The adjustment is where trust gets built. That loop (model proposes, operator corrects) is how you turn the best rep’s tacit instinct into something the whole team can run.

If you want to build this yourself, you should check out this amazing (truly) resource Sumble put together: A step by step guide to world class account scoring in under two hours.

Score → reasoning (proof) → action

A score has to carry its own story. When an account ranks high, you should be able to click straight into why. If SDR headcount drove the score, you should be able to see the 36 SDRs by name. If the model says they use Marketo, you should be able to open the exact job description where that shows up. The score isn’t the end of the work. It’s the starting point for prospecting.

The Sumble team was showing me how it worked, and we came across a real example, in Sumble’s own account model: Sigma was ranking above Deel (a much larger account). On headcount alone, Deel should win. It has the bigger sales org. But Sigma’s reps were hammering the product, thousands of queries in the last few weeks, and 11 days ago Sigma posted a job to replace legacy SaaS tools and migrate logic into their data warehouse, naming Salesforce and Clay in the post. There’s a live project at Sigma that’s directly relevant to Sumble’s product. Deel may be the bigger potential ACV, but Sigma is the most likely buyer this week.

The old approach forced you to choose: a fit score that loved Deel, an intent score that loved Sigma, and a rep left to reconcile two numbers on her own. “A man with two watches never knows what time it is.”

The newer approach bakes both into one ranked answer, with weights you can see and move. Turn the size weight up and Deel climbs. Turn it down and Sigma does. The point is that the trade-off between “how big” and “why now” is made deliberately, in the model, once, instead of being silently dumped on every rep to have to decide dozens of times a day.

Old way: Sigma is an “84”.
New way: Sigma is “high usage, a live warehouse-migration project, sufficient sales team size, and here are the people to call.”

Why data matters more for GTM teams using automation and agents

Every CRO is trying to figure out how to scale GTM with AI & automation, instead of headcount.

In the age of agents, the data underneath your go-to-market motion matters more than it ever has. A rep can open with “saw you’re moving onto Databricks, here’s how we’re different” only if the underlying data is trustworthy. When a human sends that, a wrong guess is a little embarrassing. When an agent starts sending at scale, a wrong guess is your brand, multiplied across every account it touches.

Account scoring is the first branch in that decision tree. Picture 5,000 accounts in your ICP, ranked. Your “Tier 1s” get white-glove human attention, dinners, ads, and an AE who knows their name. “Tier 3s” should ‘only’ get automated outreach via agents. Both tiers are valuable, so both need to be right.

Data accuracy impacts so much downstream, things like:

• Multi-threading

• Health scores (expansion/retention)

• Best path into accounts

• TAM and whitespace modeling

• Territory design

• Messaging

• Champion monitoring

• Risk detection

• New markets

If you get the scoring layer wrong, every one of these inherits the error since it’s the first branch in the tree.

Lessons learned from the field

Anthony has been doing forward-deployed engagements with their enterprise customers (some of the best software companies in the world, like Nooks, Atlan, and others), partnering with internal RevOps teams to build robust Account Scoring systems.

In my opinion, the company that sells a thing should be the best at using it. For Sumble, one of those things is account scoring.

Here are two customer stories and the takeaways.

Nooks ran Sumble’s Account Scoring Skill to build a model that both the RevOps team and sales team trust and can action. In the words of Michelle Idertogtokh (Senior Manager RevOps & Strategy at Nooks):

We stood this up in days, not the weeks-to-months projects of this caliber usually take: ICP definition → enriched, segment-calibrated, evaluated, deployed, and written back into HubSpot, in a few working sessions using their Claude skill in Cursor.

Easily some of the highest-leverage GTM work I’ve done.

And because the model runs across all of Sumble’s data, not just the accounts already in their CRM, it surfaced their whitespace: roughly 300 high-ranking accounts that weren’t in their CRM at all. That’s about two additional reps’ worth of territory, found for free as a byproduct of scoring the accounts they already had.

Atlan wanted something custom. They were a data catalog moving toward a semantic layer for AI, so the accounts they cared about were companies doing heavy AI and coding-agent work, the ones where they could point an agent at the right data store. They didn’t want a generic fit score. They wanted to score accounts on “AI readiness.” The model handled it, because the inputs are rich enough to define readiness in a way a binary tech-stack flag never could.

The mechanic behind all of it is a Skill. You point Claude/OpenAI at Sumble’s open-source skills repo and complete a quick setup. After that, it interviews you: who’s your ICP, what’s your email domain, do you want to read from Salesforce or HubSpot or just pull the account universe from Sumble. Then it pulls the data, builds you a local app with sliders for every weight, and lets you watch accounts move as you tune. Every score deep-links back to the raw evidence. There’s a whitespace view for the accounts you’re missing, and an evaluation view that works as a sanity check. Set up your own account scoring in under two hours.

Where this goes next

Sumble has the data infrastructure and the technical team to pull off what’s required to build s-tier account scoring models for the AI-era.

I’ve talked directly with Sumble’s team a lot on this topic and have read all their internal documents/blogs. They’re thinking very deeply about this topic, and I believe have started to crack a new way of doing account scoring. (Note: they’re working on several other deep Skills—beyond the account scoring Skill we explored today—like CRM hygiene, contact enrichment, account research and more).

Account scoring is now a live system that sits at the front of every routing decision you make, human or agent. An explainable model, calibrated to the accounts you’ve actually won and the instincts your best reps already carry, tells you exactly where to point your people and where to trust your automation. That’s the difference between a ranked call-down list reps actually work, and another “92” or “A3” sitting in the CRM that nobody touches.

PS: If you end up trying the Skill, let me know how it goes! And if you want to learn how to improve your team’s account scoring, chat with the team at Sumble—they’re all smart/awesome humans.

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

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Presented by:

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps.

Clarify → The Autonomous CRM. Sell more, admin less.

Clearskies → The Context Layer for Revenue AI.

Nooks → All your outbound. One AI workspace.

Rox → Revenue Agents for the Global 2000.

Sumble → Buying signals from tech stacks, people data, & active projects.

Terret → The answer-to-action engine for revenue teams.

Happy Monday!

Last week was an absolute doozy 😵‍💫

> anthropic and openai filed to go public
> spacex went public in the biggest ipo in history
> apple shipped what siri should have been 10 years ago
> anthropic finally dropped mythos (fable 5)
> then the government shut it down
> something big about basketball (i don’t really know)
> world cup kicked off
> new zelda and GTA 6 (apparently a big deal?)
> social network sequel trailer came out

Let’s see if this week can top that.

In other news, last Wednesday, we held the “Anonymous GTM Tech Demo Day.” Almost 300 people joined us live (700 registered). I spent the last few days building out this post as a resource for anyone who wasn’t with us live to skim over the 22 startups* featured.

*Important note: all of these companies are “startups” (where lots of innovation is happening) defined as having less than $5M ARR and less than $50M in funding.

Final things worth noting:

• Format: Live, 3-minute demos from real customers, not the founder.

• None of these companies paid to participate.

• These are some of the coolest startups building at the bleeding edge of GTM & AI.

• Full Playlist (YouTube) of the 22 demos (this was a marathon!).

If you end up taking a demo of any of them, let them know you found them on The Signal! (I don’t get a referral bonus, just want to share the love!).

Let’s dive in.

Top of Funnel (9)

Signal Intelligence:

Alfa

⤷ Alfa finds champions for anything you sell.

Masumi Watanabe (VP Revenue Operations at Phocas) demo’d Alfa.

My favorite quote from their demo:

You’re not drowning in a bunch of filters anymore. You just talk to their agent about who you want to go after.

Watch Alfa’s demo here (3.5 mins).

Saber

⤷ Saber is real-time signals infrastructure that surfaces in-market accounts the moment they show buying intent, so sales teams reach out at exactly the right time.

Simon Hoffenzits Lønstrøm (Head of Revenue Operations at All Gravy) demo’d Saber.

My favorite quote from his demo:

[Our reps] set this up, run it on 40 accounts, and then they go grab a coffee. When they’re back, they have 40 accounts ready to dial.

Watch Saber’s demo here (3 mins).

Smoke Signals AI

⤷ Alpha signal GTM systems powered by people and a suite of AI tech.

Evan Dunn (Head of Marketing at TitanX) demo’d Smoke Signals AI.

My favorite quote from his demo:

Dynamic AI-powered nurture is a forgotten channel for most folks.

Watch Smoke Signals AI’s demo here (2.5 mins).

Syft AI

⤷ Syft finds companies actively struggling with the exact problem your product solves, then tells your sellers or ai agents exactly who to engage and why.

Brian Jennerich (Account Executive at ClearBlade) demo’d Syft AI.

My favorite quote from his demo:

Four of my eight deals this quarter were generated through Syft. They were all from companies I’d never heard of.

You miss 100% of the revenue that you’re not aware of.

Watch Syft AI’s demo here (3 mins).

Syft Data

⤷ Convert warm prospects into the pipeline with website, LinkedIn, and product intent signals.

AJ Alonzo (Director of Marketing at DemandDrive) demo’d Syft Data.

My favorite quote from their demo:

Knowing that an account visited our website and what pages they went to doesn’t tell us why we should be reaching out.

Motions go from “I saw that you were on our website” to “it looks like you just overhauled your entire go-to-market team, which tells me you need pipeline yesterday.”

Watch Syft Data’s demo here (3.5 mins).

WhiteWhale

⤷ Know which accounts to target every day and why.

Tom Grainger (Founder at Advanced Client) demo’d WhiteWhale.

My favorite quote from his demo:

We can build completely custom signals, things like hiring SDRs, a new RevOps hire, new sales leaders. Anything that’s out there is completely custom, and you can build it out with natural language.

Watch WhiteWhale’s demo here (3 mins).

Outreach:

Exportly

⤷ The Clay-powered Chrome Extension for AI-Native Sales Reps.

Meghan Prudencio (Senior GTM Engineer at Webflow) demo’d Exportly.

My favorite quote from her demo:

The best sales plays live in tools like Clay, but our reps are living in LinkedIn, Salesforce, Outreach. All that power never really reaches them in a form that they’re actually using.

Watch Exportly’s demo here (4 mins).

FirstTouch

⤷ Operationalize Social inside HubSpot so you can automate, track, and attribute on it like a primary GTM channel.

Adia Toll (Head of Sales Development at AskElephant) demo’d FirstTouch.

My favorite quote from their demo:

We have a 98% reply rate on things that start with social.

Watch FirstTouch’s demo here (2 mins).

skyp

⤷ AI-powered outreach for high-value B2B sales. High deliverability built in.

Scott Desgrosseilliers (Founder & CEO at Wicked Reports) demo’d skyp.

My favorite quote from his demo:

This is 250 people, 97 opened. That’s sometimes better than an email on a warm list.

Watch skyp’s demo here (3 mins).

Middle of Funnel (5)

Engage on the Website:

1mind

⤷ 1mind deploys autonomous AI Superhumans across your entire customer journey to engage, qualify, sell, and support in every channel so buyers get one seamless, expert and delightful experience from first touch through renewal.

Steven Birdsall (CRO at Alteryx) demo’d 1mind.

My favorite quote from his demo:

Think of [Annie] as your best SDR, Atlas as your best SE, and Adam as your best CSM.

Watch 1mind’s demo here (4.5 mins).

Docket

⤷ Docket is your Inbound Demand Capture Agent. It engages your website visitors in human-like conversations that turn inbound traffic into qualified sales pipeline.

Scott Henderson (Senior Digital Marketing Manager at Galileo Financial Technologies) demo’d Docket.

My favorite quote from his demo (he asked Docket’s agent whether it could support another country, and it switched languages on the spot):

Claro, también puedo ayudarte en español.

Watch Docket’s demo here (3 mins).

Knock AI

⤷ Knock AI replaces static forms and follow-up emails with instant conversations, so you can engage high-intent buyers anywhere they show up. It qualifies, enriches, and routes them, helping marketing teams turn demand into pipeline before buyers disappear.

Ranny Bloch (Growth at Guardz) demo’d Knock AI.

My favorite quote from his demo:

The other day I jumped in and the guy said: I know it’s nice that you’re human, but can I go back to the bot, please?

Watch Knock AI’s demo here (4.5 mins).

Relationships & Account Mapping:

Centralize

⤷ Centralize is a deal GPS that automatically maps every relationship across your accounts and tells reps the next turn to make.

Samantha Lee (Director of Commercial Sales at LangChain) demo’d Centralize.

My favorite quote from her demo:

It used to take me at least five hours to put together an org chart. I built one for a 10,000-plus-person semiconductor company within an hour.

Watch Centralize’s demo here (4 mins).

The Swarm

⤷ The Swarm curates a rich dataset of 500M profiles with daily job changes, 50M companies with fundraising data, and a unique relationship mapping AI engine for builders and investors.

Richard Purcell (Marketing at Built) demo’d The Swarm.

My favorite quote from his demo:

Referrals are the highest performing channel across verticals, but they’re the least predictable.

Within 24 hours of passing this list to one of our SDRs, they booked a meeting with an EVP at a $2 billion company.

Watch The Swarm’s demo here (3.5 mins).

Bottom of Funnel (3)

Win the Deal:

Ruby

⤷ Ruby is the first always-on sales agent team - automatically gathering the full picture, building memory, and delivering contextual coaching and content in every deal, so every rep closes like your best one.

Adam Lebel (VP Sales at Axya) demo’d Ruby.

My favorite quote from his demo:

30 agents, all autonomously working together. Reps are supported throughout the entirety of the sales cycle.

Watch Ruby’s demo here (4 mins).

Terret

⤷ Terret is the AI-native answer-to-action engine for sales teams – turning any question into instant analysis and automated action for every role, from leadership to rep.

Matt Durazzani (VP of Revenue Operations at Litify) demo’d Terret.

My favorite quote from his demo:

All of a sudden, we have this extension of leadership in every deal.

Watch Terret’s demo here (7 mins).

Coach & Ramp Reps:

Avarra

⤷ Scale your best coaches and accelerate rep performance with personalized coaching and AI-driven simulations, at a fraction of traditional enablement costs.

John Harper (Senior Sales Leader at Paychex) demo’d Avarra.

My favorite quote from his demo:

Our average first year run rate before using Avarra was about $22,000 ARR. After using Avarra, our average run rate for new hires is about $96,000 ARR.

Watch Avarra’s demo here (3.5 mins).

Foundation (5)

Cedar

⤷ Cedar is the AI-native workspace for Account Executives. We help AE’s close more deals by finding the winning playbook and helping them execute it.

Sam MacGregor, Head of Growth at Simcare.

My favorite quote from his demo:

I need to automate everything because I can’t be typing out emails, I can’t be doing customer support. Cedar just acts as my brain for everything: it knows our entire company playbook and gets all the information across all our inboxes.

Watch Cedar’s demo here (3.5 mins).

Clearskies

⤷ The context layer for revenue AI.

Patrick Severs (Head of Revenue and Human Data at G2i) demo’d Clearskies.

My favorite quote from his demo:

We can’t do any of this unless Clearskies is in the middle doing the context work.

Watch Clearskies’ demo here (5 mins).

Deepline

⤷ Go-to-Market SDK + CLI for Agents - run your GTM motion from Claude Code to production, every provider on one API key.

Sujay Choubey (Growth at Composio) demo’d Deepline.

My favorite quote from his demo:

Because it exposes as a CLI, we could control the waterfall and sequence enrichment vendors to optimize our costs.

Watch Deepline’s demo here (3 mins).

Octave

⤷ Octave is the real-time context engine that helps GTM teams build smarter agents faster.

Heath Barnett (VP, Revenue at MixMax) demo’d Octave.

My favorite quote from his demo:

Octave is basically the GitHub of our ICP.

Watch Octave’s demo here (2.5 mins).

Swan AI

⤷ Your AI GTM Engineer.

Brianne Thomas (VP of Marketing at Elastic Path) demo’d Swan AI.

My favorite quote from her demo:

Swan has been a huge, huge time-saver for our sales team. They’re obsessed with it. I’ve never launched a tool before that our sales team has actually, like, wanted to use, and Swan is definitely one of them.

Watch Swan AI’s demo here (4.5 mins).

Other portals of the web I explored last week:

• The AI paradox: More automation, more humans, more work (Dan Shipper on Lenny’s podcast) ← if there’s one piece of content you consume, it’s this one (summary of 10 things from the pod)

• A few observations from deep in the trenches of GTM AI land over the last couple months by Nikko Georgantonis, Head of GTM AI & Systems at Hightough

• Davide Grieco, Head of Growth at Clay said, “I’m here to tell you your tech stack doesn’t matter. The entire GTM world is obsessing over AI agents and workflows. Meanwhile, the best marketers are doing the opposite.” Spicy (and 4k likes, so clearly hit a nerve).

• The AI-native GTM playbook is not “replace everyone with agents.” Great conversation with Replit’s CRO on Kyle Norton’s podcast.

• Avoiding Death on the Yellow Brick Road (Why The App Layer Isn’t Dead)

• On Grindslop (Notes on founder hagiography and aristocratic virtue) by the goat, Will Manidis

• The Simple Genius of Rick Rubin I can’t get enough David Senra or Rick Rubin these days (so this collab was a dream come true for me).

• SpaceX Launches Largest Ever IPO | OpenAI Files to Go Public | Uber Cuts 23% of HR (20VC with Harry Stebbings, Jason Lemkin, and Rory O’Driscoll)

• Someone built an app that monetizes the wait time on LLMs lol genius

• I’m still thinking about this one…

  

That’s it for today.

Have a great week, GTM nerds.

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

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Presented by Clay.

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps

Clarify → The Autonomous CRM

Nooks → Agent Workspace for Intelligent Outbound

Sumble → Buying signals from tech stacks, people data, & active projects

11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

Today’s piece is a collaboration with Patrick Thompson, who writes Founder Therapy and is the Co-founder & CEO of Clarify. More on his newsletter at the end.

As a founder, you’re under real pressure to set the pace on AI inside your company (and scale with AI & automation instead of headcount). Your team watches how you use it. Your board asks what you’ve automated. And it’s hard to tell a real workflow from demoware on LinkedIn.

The most AI-pilled companies I see have a very AI-pilled CEO. The pace starts with the founders.

So here are 27 specific ways founders can use AI today, across:

• Revenue and customer-facing

• Marketing and content

• Product and the feedback loop

• Running the company

• Hiring

• Fundraising

Alright, let’s get into it.

Revenue and customer-facing

1. Build your ICP from your calls.

Feed an agent your prospect and customer call transcripts and have it build a specific ICP and buyer-persona sheet from what buyers actually say, not what you guessed at an offsite.

2. Signal-based outbound, founder-led.

An agent watches your ICP for the moments that predict a deal (social listening, a key hire, a competitor in their stack, a telling job post), then hands you a short list + a pre-drafted email to review. You still do founder-led outreach, but the signal-watching agent runs the research for you. (More ideas here: the best AI-native GTM plays you’re not running.)

3. Champion tracking.

Your best next customer is the one who already knows and loves your product. This is especially true in the early days. So, when a past champion changes jobs, an agent flags it and drafts the note to reach them in their new seat (this play will often lead to your fastest new logos).

4. Lookalikes of your best customers.

An agent builds a target list that mirrors your closed-won accounts, so you start from who already works, not a search using finger-in-the-air firmographic filters.

5. Warm intros.

Point an agent at your collective network (LinkedIn, Gmail, calendar, plus investors, advisors, and team), have it find who can introduce you into a target account, and draft the ask.

[Pro tip: export your LinkedIn messages and have Claude score them for sentiment, and wire up the Google APIs for calendar and email. I’ve done both, and it’s magic.]

6. A pre-call brief on every inbound.

The moment someone books a demo, an agent turns their funding, stack, recent news, and anything else relevant for you, into a simple brief, so nobody is scrambling to research the account three minutes before the call starts (or three minutes into the call… we’ve all been there).

7. Post-meeting follow-up that moves the deal.

After a call, an agent drafts the recap and the explicit next step toward closing, sent within the hour instead of three days later.

8. Closed-lost, analyzed and reopened.

Monthly, an agent clusters why deals actually died and drafts the re-engagement note when something changes.

9. A battlecard that updates itself.

An agent watches competitors’ pricing, changelogs, and G2 reviews and keeps your battlecard current instead of stale since March.

(The person who builds and owns most of these is usually a GTM Engineer, and most teams scope that role too narrowly. Here’s why. But in the early days, the founders should personally be building these agents.)

Marketing and content

10. Build your founder brand (idea-miner and editor, not ghostwriter).

If your buyers live on LinkedIn or X, use AI to mine post ideas from call transcripts and internal docs, and as a custom editor on your drafts (I wouldn’t let it ghostwrite yet). The same setup turns one post, talk, or call into a week of content in your voice. (More here: How to build a founder brand with the Rick Rubin of LinkedIn, Alec Paul.)

11. Personalized assets and decks.

Use Claude Design, Gamma, etc to spin up tailored sales-demo decks, marketing and event assets, even investor decks and internal memos.

12. Case studies from your first customers.

The moment you have a couple of paying customers, interview them and have an agent turn the call into a personalized case study with pull quotes. If you want to go deeper here, my friends Noah and Adrian recently published a great walkthrough: Building a Social Proof Engine.

13. Partner activation on autopilot.

When a partner goes live, an agent builds their landing page and drafts the welcome email with the link and promo code. (Patrick’s team runs this inside Clarify, on Sanity and Stripe.)

Product and the feedback loop

14. Feedback into a ranked roadmap.

Every ticket, sales objection, and churn note gets tagged by feature request and ranked, so the roadmap follows aggregated data, not the loudest customer.

15. A PRD from a voice memo.

Talk an idea through on a walk, and an agent returns a structured spec with edge cases and open questions. (Capture it with something like Superwhisper and it’s close to free.)

16. Support triage and first-draft answers.

Conversations sync from your help desk hourly, are deduped, and an agent drafts answers from your docs for a human to approve.

Running the company

17. The morning brief.

A daily brief across your inbox, calendar, CRM, and Slack, so you show up prepared by default.

18. A daily “what did the agents do” report.

Each morning, a Slack summary of the last 24 hours of agent runs and time saved. Visible, repeated wins are what make AI adoption actually stick on a team. (As you scale agents, this step will become more important.)

19. Decision memos on demand.

Talk through a hard call and get back a structured memo with the options, the tradeoffs, and a recommendation to react to.

20. Board updates and the metrics narrative.

An agent assembles the monthly board update from your metrics, CRM, and product data, and you query the numbers in plain language (”why did net revenue dip?”) instead of clicking dashboards.

Hiring

21. Sourcing, not just screening.

Talk to Claude about the exact person you want (eg: the companies they came from, their current role) and have it run deep research to source specific candidates. You can do this instead of using a recruiter or to augment a recruiter’s efforts.

22. A work sample that allows requires AI.

Let candidates use AI in the coding round, and its equivalent for every role. You’re hiring for real problem-solving in 2026, not for who can pretend ChatGPT doesn’t exist. Hire AI-natives.

23. Role briefs and screening, drafted.

Draft the internal role brief that defines what great looks like, then have an agent screen inbound applicants against it for consistency.

Fundraising

(Note: Treat fundraising as sales and these map cleanly onto a sales workflow.)

24. Record the calls, sharpen the pitch.

After each investor call, an agent recaps what landed and drafts a tighter version of the pitch for the next one.

25. Cluster objections into a battlecard.

An agent groups objections across calls (valuation, market, traction, team, competition, moat) and maintains a living battlecard with your best responses and proof points.

26. Run the raise like a sales pipeline.

A lightweight pipeline of targets, status, and next steps, with a weekly summary of what you’re learning, so your pitch and process improve as you go.

27. Prep and follow up for every meeting.

Before the meeting: get info on current portcos, where the company is investing, the partner’s thesis, tailored talking points and questions. After the meeting: a follow-up draft with the materials and deadlines they asked for.

Where to start

The founders pulling ahead have moved a dozen of these off their plate, so the company runs when you’re heads down, on a flight, or out for a week.

Don’t build all 27. Start with three: the workflow you hate most, the one that always gets skipped, and the one closest to revenue. Watch them for a week, then add three more.

Once data capture stops being a human job, the interesting questions become: what is the human best used for, and what does your software even look like when agents do more of the operating? I think that reshapes the tools we live in, starting with the CRM. More on that in a post that will be published later this month (“How SaaS UI is evolving”).

If you got something out of today’s piece, half the credit is Patrick’s. He writes Founder Therapy, a weekly, honest read on what building a company actually feels like. It’s a phenomenal Substack (I read it). If you’re a founder, subscribe here:

Founder Therapy

A weekly newsletter by Patrick Thompson, co-founder & CEO of Clarify. Honest reflections on the ups, downs, and in-betweens of startup and founder life.

By Patrick Thompson

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Subscribe now

Presented by Clay.

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps

Clarify → The Autonomous CRM

Nooks → Agent Workspace for Intelligent Outbound

Sumble → Buying signals from tech stacks, people data, & active projects

11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

Today’s post is by my friend, the legend, Eric Linssen. He’s a great marketing operator, and now he runs a private community (Demand Collective) of 650+ demand gen leaders from cos like Glean, Cursor, Datadog, and ElevenLabs. (He’s also been instrumental to me as I’ve built The Signal into ‘a real media company’… I can hear him laughing as I type that.)

Demand Collective runs incredible free virtual summits, like the “Demand Day Summit” coming up on June 18th from 10am - 1:30pm PST. 8, no-fluff 20-minute sessions, from operators at companies like Ashby, Superside, and Sentry.

See the agenda & register (it’s free) here.

He has interviewed 75+ demand practitioners about what’s actually working for them. And from those conversations, he’s collected dozens of what he calls “hidden multipliers”. Small, simple tweaks that can get you more growth without more budget. Today, he’s sharing 11 of the most useful ones he’s found.

Let’s get into it (over to Eric).

TL;DR: 11 Hidden Demand Gen Multipliers

There are no silver bullets (in demand gen), only lead bullets.

Our marketing’s impact has a ceiling dictated by forces outside of our control (PMF, market trends), but these hidden multipliers are lead bullets we can put to work to get as close to that ceiling as possible.

I’m calling these “hidden multipliers” because they don’t take extra budget. They’ll help you multiply your impact, without spending any extra budget on growth.

1/ ICP: Closing the ICP translation gap so you waste less spend on non-ICP accounts.

• Problem: You might be wasting spend on non-ICP accounts. Because you’re either using native platform targeting, or account lists built w. firmographic filtering.

• Root Cause: These tools are blunt. They think Bumble and Cursor are both “software development companies”.

• Solution: Use a tool like Clay to score your target accounts with much more granularity, so you can massively decrease wasted spend on non-ICP accounts.

Example/Go Deeper: How Tyler Calder, CMO @ PartnerStack increased ACV 58%+, while decreasing cost per dollar of pipe by 35% by “closing the ICP translation gap”.

2/ Person-level targeting audit

• Problem: Even if you have the right accounts, if you’re using job title filters in LinkedIn, you might be wasting spend on the wrong people.

• Root Cause: LinkedIn job title filtering sucks. Meta’s is even worse (it has none lol).

• Solution: First, you can pull a demographic report to see if you’re wasting spend on the wrong titles. If so, you can use a tool like Vector to target by specific people, across every channel.

• Example/Go Deeper: How Airbyte improved pipe:spend from 1.5:1 to 4:1 by fixing their targeting.

3/ Fix your confusing website positioning.

• Problem: You could be spending money getting qualified prospects to your website, but they leave having no idea what you do. Make sure everyone coming to your website knows *exactly* what you do, for who, and why you’re better than alternatives.

• Root Cause: Who knows, but this is true for 80%+ of b2b companies.

• Solution: Follow Fletch PMM’s positioning playbook.

4/ Fix your broken content.

• Problem: If your content (ads, blogs, resources, webinars) isn’t converting, you’re wasting ad spend & energy distributing it.

• Root Cause: It’s probably one of the root causes above👆.

• Solution: Make sure your content “is good, product-focused, and distributed well”.

Example/Go Deeper: Mary Keough’s framework (w. examples).

5/ Manual bids on LinkedIn

• Problem: If you’re not running manual bidding on LinkedIn, you’re probably wasting spend and overpaying for impressions, so your spend won’t go as far & CPA will go up.

• Root Cause: LinkedIn’s maximum delivery (or manual bids that are too high) will over-bid because LINKEDIN LOVES YOUR MONEY!

• Solution: Either start w. Lower-than-recommended manual bids and inch up until you get delivery, or use an automated tool like Vector’s bid agent.

6/ Test “micro-campaigns” in your outbound

The ‘playbook’ Brendan calls “Micro-campaigns” (well… this is meta…):

• Timing is more important than anything else in outbound. A ‘mid’ message to the right person at the right time beats a perfectly crafted message to someone with no need.

• The smartest outbound teams are using AI to find these people (tiny lists of people with a strong reason they’re likely to take a meeting NOW).

• They send them hyper-personalized messages with a no-brainer offer.

• And they repeat every week to new, hyper-targeted lists as new prospects move “in-market”.

It’s worth testing micro-campaigns with a small % of outbound budget against control and see if you can improve total throughput.

It’ll probably work. The volume is low, so you won’t need to hire anyone new, just reallocate a small % of the rep time already doing outbound.

7/ Test incentivized demos on BOF campaigns

Incentivized demos get a lot of flak, but for lots of Demand Collective members I talk to… they work. Not cold for most companies, but if tested against existing BOF “Get a Demo” ads, they out-perform them (must make sure targeting is dialed in).

So it’s worth running a small test comparing incentivized demos vs. regular demo request/BOF ads.

8/ Showcase customers in organic channels

This one takes the most resources out of the 11, but it’s worth calling out. If you’re already spending money on organic, highlighting existing customers is probably the highest-leverage way you can improve the channel. You may be able to learn from what worked for Nooks, Userled, or Jess Cook:

• Nooks’s organic LinkedIn playbook.

• Userled’s customer advocacy campaign

• Jess Cook’s influencer playbook.

9/ Cut wasted spend on Google Search

Setting up keyword-level ROI tracking takes half an hour. Once you have it you can see which specific deals came from which keywords. And also, which keywords you’re spending on that aren’t driving any downfunnel results (so you can cut them).

Full breakdown here.

10/ Win other high-intent discoverability channels

Chris Shuptrine, VP Mktg at Torii, scaled Ent leads 90%+ QoQ through what he calls “High Intent Discoverability”. He:

• Chooses a “phrase” he wants his buyers to associate him with (for Torii, “SaaS Management for IT, Finance, and Security”)

• Bids/focuses on organic *everywhere* a target prospect would search for that phrase. Google, Bing, Reddit, Capterra, G2, Quora.

• All so Torii “is seen 5+ places within 5 minutes of research.

Even if it’s $5/day, those high-intent discoverability channels are the highest ROI money you’ll spend if you have any sort of mature category, because you’re only bidding on the highest-intent searches.

Hope you found these valuable!

Thanks again to Eric for this guest post! He’s a legend, a terrific operator, an appreciator of both AI—and human craft, and he runs the world’s best community for Demand Gen practitioners. Here are three ways to go deeper with Eric/Demand Collective:

• Subscribe to his newsletter: The Demand Collective Newsletter.

• Apply to join The Demand Collective community (it’s free, but you have to be spending $30K+/mo on ads).

• Register here to attend Demand Day Summit (June 18th).

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

PS: Still want more? Best of The Signal

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Presented by Clay.

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& Supported by:

Attention → AI Agents for RevOps

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Nooks → Agent Workspace for Intelligent Outbound

Sumble → Buying signals from tech stacks, people data, & active projects

11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

I ran Operations & Enablement for the Global BDR Org (~125 people) at Zoom Video in 2020-2021. But a lot has changed with Sales Enablement since then.

So, I was excited to catch up with Mercy Bell, who ran Enablement at Webflow and has spent 15 years in RevOps + Enablement orgs and consulting with large, complex teams at Meta, Outreach, Retool, and more. And is a Stanford alum. I believe that most (not all, but most) of the best Sales Enablement folks came up as sellers themselves (Mercy started as an SDR at Google, then AE at Kahuna).

She made an off-handed comment during our conversation that I haven’t been able to shake: we are putting more care into architecting environments for our AI agents than we are into developing the humans who still run our go-to-market motions.

That’s what we’re going to unpack today. Specific frameworks, best practices, and programs. All backed by science (yes, science! from psychology research papers and old textbooks). Here’s what we cover:

• What enablement actually is (retrieve, recall, resist)

• Why the 2010s Enablement playbook is dead

• The agent parallel

• Why sales teams will get incredibly small

• Self-determination theory and why it matters for GTM

• Four programs every GTM org needs to build for humans

Alright, let’s get into it.

What enablement actually is (retrieve, recall, resist)

Mercy defines enablement through a framework of three R’s.

The first is retrieve. Can your people find what they need? Information, content, competitive intel, whatever. This is the systems and tools question, and it’s the one most companies default to solving. It’s also the easiest. RevOps can own a lot of this.

The second is recall. This is harder. Can your reps, in the moment, pull up the right heuristic, match tone to context, read the room, etc.? As Mercy put it: “What they need is to be able to recall. The human brain takes in so many factors. The fact that when the call got picked up, they heard the tone of voice was a little low energy and they matched it. They understand that person’s seniority level and role means a certain kind of shifting of how you position a question.”

Recall is the stuff that separates your top performers from everyone else. It’s pattern recognition built through repetition, not a knowledge base article.

The third is resist. Judgment. Impulse control. Knowing when not to oversell, when to walk away from a deal that doesn’t fit ICP, when to shut up and listen. “Discernment and judgment is mostly about what we don’t do, not what we do,” Mercy said.

This framework matters because, in my observation, most enablement teams spend the majority of their time on retrieve and almost no time on recall or resist. They have it inverted.

Why the 2010s Enablement playbook is dead

If you built an enablement team in the last decade, it probably had a content person, a systems person, and a trainer. That structure made sense when the playbook was stable, the tools moved slowly, and compliance meant keeping an LMS fresh.

That world is gone.

There’s absolutely no way to apply the 2000s, 2010s model of enablement around compliance and documentation to what’s being built and deployed inside every go-to-market org right now.

When she was interviewing for in-house roles, she kept asking: what would I be coming in to do? The answer was always “activate the field.” But a layer deeper, it was everything. Systems. Behavior change. How humans work alongside the technology they’re now riding on top of.

The people who are still doing last-mile training and keeping content fresh? They’re going to get replaced. The ones who survive will go one of two directions: into systems and operations (GTM Engineering, RevOps), or into coaching, recall-building, and leadership development.

The era of the generalist enablement hire is over, and if you’re not sure whether you need an enablement person, the right move is to diagnose the root cause first (it might be a product marketing gap, ICP clarity, or managers who can’t coach) and then hire a specialist to solve that, not a generalist to cover everything.

The agent parallel

Every founder and sales leader wants to scale GTM with AI and automation. I write about this constantly. Mercy expanded my perspective here.

Think about how much care goes into building a good AI agent. You’re constructing context windows. Giving examples of what good looks like. Reviewing outputs one by one, grading them, explaining why one was good and another bad. You’re architecting the entire environment for that agent to succeed.

Now ask yourself: are you doing the same for the humans on your team?

“We’re applying all this compassion and diligence to architecting environments for agents to do their best work,” Mercy said. “We’re not doing the same for the human beings. Most people I know right now are burnt out, anxious, a little bit confused about what their world is.”

Execution is a sales team’s moat. So, leaders need to get honest about where their people actually are. Not just their AI strategy or tech stack. Their people.

Why sales teams will get incredibly small

Mercy thinks that in 5-7 years, sales teams will be incredibly small, and managers will be true player-coaches, still carrying a book but spending far more time developing their people. Investment in human skills will go way up, especially where deals are complex and relationship-driven. The art of the one-on-one will matter more than the dashboard.

“The renaissance of enablement is world-class enablement of frontline leaders to do things like the art of the one-on-one,” Mercy said. “That is actually an art form. And it’s also science.”

Self-determination theory and why it matters for GTM

If you accept that investing in humans matters, the next question is how. Mercy’s framework draws from research that’s been around since the 1960s (I told you we were going to be looking at old research papers today!).

Self-determination theory (SDT), developed by Edward Deci and Richard Ryan, identifies three universal psychological needs that drive motivation across cultures, generations, and roles.

Autonomy is the need for meaningful choice. Not doing whatever you want, but feeling like you’re directing your own work.

Competence is the need to feel effective. This one has a trap. “Everyone gets a sensation of competence when they use AI,” Mercy warned. “It’s addictive. But that feeling of competence isn’t the same as actual competence.”

Relatedness is the need for genuine human connection. This is the one Mercy is most passionate about. “All of this tech can feel so isolating.” Teams and managers that foster real connection outperform, and it’s not a fuzzy concept. It’s neuroscience. Dopamine. Serotonin. The real stuff.

SDT also provides a motivation continuum. On one end: amotivation (no drive). Then external regulation (I work to get paid or avoid punishment). Then increasing stages of internalization before you reach intrinsic motivation, where someone does the work because they love it.

“The companies that are crushing it right now are almost exclusively in that identified-to-integrated range,” Mercy said. “You can’t run your business on fear right now.”

So if those are the psychological needs, what do the actual programs look like?

Four programs every GTM org needs to build for humans

Mercy outlined a four-program architecture for developing the people running your GTM motion.

1. Ramp (not “onboarding”)

“Onboarding” implies someone hops on board, and then they’re done. Ramp is the gradual process of bringing a human from “can’t do anything” to “can hit the number.” Neuroscience shows us that when something is new, the brain is at its most open and receptive. That window closes quickly.

Mercy suggests to build ramp around the customer journey, not the product. Tell the story of the customer from lead to renewal. Frame everything from the customer’s perspective.

And don’t do role plays in the first two weeks. Instead, “train their ear.” Capture a ton (like 60-70+) great clips of real sales calls where just the prospect is speaking. Play it, stop it, ask them what they heard.” Build the recall muscle early. This is hard work. There is no agent for it. And it’s worth doing specifically at companies with “high-trust sales cycles,” where the stakes of getting it wrong are six (or even seven) figure deals being lost.

2. Product and solution depth

Most companies train on product at the same altitude for every role. An AE, an SDR, and a solutions engineer need to know different things at different depths. Get specific about who needs to know what.

3. Skill development

Develop the fundamentals. Discovery. Giving a great demo. Negotiation. These are muscles that need consistent repetition, not a quarterly drop-in from an outside trainer. “The human brain requires repetition and feedback. It’s got to be an always-on thing.”

4. Leadership development

This is the one that gets punted to L&D and then mostly ignored. But everything above gets delivered by frontline managers. If those managers aren’t being developed, none of the rest works. How do you give hard feedback? How do you coach in the moment? These skills come from ongoing, repetitive development, not some random, one-time certification.

In 18 months, the best GTM orgs won’t just have the best agent infrastructure. They’ll have the best human infrastructure, too. The same rigor they’re applying to context windows and evals, they’ll be applying to ramp programs, coaching systems, and leadership development. Sales orgs that invest in both layers (agents and humans) are going to run circles around the teams that are over-indexing on agent infrastructure and overlooking human infrastructure.

That’s the Renaissance of Enablement.

Thanks to Mercy Bell for sharing her frameworks and for being the kind of practitioner who reads academic papers for fun (legend). She’s spent 15 years inside RevOps + Enablement orgs. If you’re looking for guidance on how to build an Enablement program in 2026, you should reach out to Mercy, she’s an awesome human.

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

PS: Want to share The Signal with a friend? Send them this to skim: Best of The Signal

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Presented by Clay.

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& Supported by:

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11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

I’m trying a different format today.

Maybe this will become a recurring monthly (?) thing. I’m experimenting :) Let me know if you like it or don’t like it (my feelings won’t be hurt!).

GTM-tech funding/news

It’s been a wild month in GTM-tech land so far.

Gong passed $500M in revenue, while growth has accelerated (according to my quant, that’ll put them at $800M in 1 year, so IPO is clearly in sight; that’ll be a big win for GTM-tech).

Actively AI announced their Series B. We first wrote about Actively AI when they raised their Series A in April of 2024: The AI Research Company Building GTM Superintelligence.

Monaco announced their Series B ($50M) just 3 months after announcing their A. Bonkers. Before starting Monaco, Sam Blond (CEO) was the VP Sales at Zenefits, then CRO of Brex, followed by a brief stint as a capital allocator at Founders Fund. And notably, one of his co-founders, Abhishek, is the previous CPO of Apollo.io. They’re building an all-in-one sales platform.

I announced I’m officially full-time on The Signal. I’m building this into a proper media business :) Exciting and scary plunge (with three young kids). But yolo. Thanks for the support. Speaking of, I’ll be partnering with a limited number of brands for the next 6 months. We should talk this week if you’re interested (reply to this email or dm me on LinkedIn).

The Signal, featured recently:

• [Growth Unhinged by Kyle Poyar] → The best AI-native plays you’re not running (Our second collaboration)

• [30 Minutes to President’s Club] → How to Use AI to Identify the Perfect Time to Prospect

• [Topline] → GTM Engineer: The Most Misunderstood Role in Go-to-Market by Brendan Short [Topline] | A framework for revenue leaders deciding whether to build this capacity now, later, or not at all

GTM content of the month:

• Kyle Norton (CRO at Owner.com, and friend of The Signal) gave a kind shout-out to me/The Signal in his talk at SaaStr this week. Super cool to see from such a goat in our space. His entire talk is worth listening to: AI-native GTM 101: The 5 AI Decisions to Get Right

  

• Who’s actually hiring in GTM right now | The companies selling AI are the ones doubling their SDR headcount on Growth Unhinged with Kyle Poyar

  

• IC work is the new career flex | The rise of the High-impact Individual Contributor (HI-C!) by another one of the goats, Elena Verna. Here’s my take on this trend.

• Sell the Truth | On persuasion, charisma, dealmaking, and walking away (podcast episode by Naval)

• Firsthand: How I Bet On Clay (And It Bet On Me) | I joined Clay as one of its earliest employees and a reluctant marketing hire. This is how, over the last three years, we built a role around my strengths as a storyteller, scaled past $100M ARR, and created a brand that startups study. by Mishti Sharma

• Agentic workflow (by Jerry O’Shea) that does the heavy lifting before a rep touches an account using a bunch of tools connected together:

  

• Knowledge bomb by Todd Bussler (former Co-founder & CEO of Champify, recently joined Clay as an Enterprise Sales Leader) → “90 days into a new job, everyone loves to post about their 5 biggest learnings. I’m doing the opposite. Here is everything I had to UN-LEARN going from a sales leader at a traditional SaaS company to an AI-first company like Clay”.

• 10 memes (h/t Will Aiken) for you to Slack to your co-worker (instead of updating Salesforce before your forecast meeting later today)

  

Oldies, but goodies (from the archives)

The Signal has added ~2,000 new subscribers in the past 90 days (roughly 30% growth), so I thought it would be worth (re)sharing a few older posts from the archives.

• The “System of Intelligence” The biggest opportunity in GTM tech

  This was true in Oct 2024 when I wrote this piece. And even more true today, as AI has continued to ramp. There is still no clear winner (though, there are many attacking this problem). Excerpt:

  

  

• The Rise of the GTM Engineer (published Nov 2024). Excerpt:

  I am hopeful for the future of modern go-to-market. I think it will include more intelligence and creativity injected throughout it. And, no matter what this name of this new role ends up being, I think these new “jobs to be done” are going to grow in importance in the coming years. We’ll find out!

  …The question in my mind is simple: will this number be higher or lower this time next year? What about in two years? My bet: it will be much, much higher.

  Crazy to see this early data from back then (here’s what I said in the article:

  

• My AI Journey: Not One Lightbulb Moment, but 100 Sparks that Gave Me Deep Conviction (& why you’re not behind on AI) + My top 11 lessons from my AI journey so far. Excerpt:

  

• (Last week’s post) How Two of Rippling’s Marketing Pioneers are Building a GTM Engine from Scratch (I Was Surprised) | With Brandon Camhi & Noah Adelstein. Excerpt:

  

Alright, that’s it for today!

Reply to this email and let me know what you think of this (monthly?) format.

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

PS: Want to share The Signal with a friend? Send them this to skim: Best of The Signal

Subscribe now

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Presented by Clay.

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps

Clarify → The Autonomous CRM

Nooks → Agent Workspace for Intelligent Outbound

Sumble → Buying signals from tech stacks, people data, & active projects

11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

I’m excited to continue to shine a light on the smartest GTM operators today. I had the pleasure of interviewing two of the best in the game recently. And wrote this piece (with their feedback). It was surprising and refreshing. Anyone building a modern GTM engine should learn from them. I hope you enjoy it.

Brandon Camhi joined Rippling in November 2019 at ~150 employees and $10M ARR as the first growth marketing IC. Over the next six years, he then built Rippling’s growth team, and went on to lead all of marketing through nearly $1B in ARR. Noah Adelstein was his Head of International and Automated Growth.

Earlier this year, Brandon joined Netic (a Founders Fund and Greylock-backed AI revenue engine for essential services) as their COO. Noah followed him to lead Growth (and also writes an amazing newsletter, the gtm engineer).

These two helped build the GTM playbook at Rippling to a massive scale. And now they’re building the machine again, from scratch. Fresh slate. With AI at their disposal.

That’s why I saw it as a unique chance to hear first-hand how two people who have “been there, done that” are doing it with all the latest in 2026 (roles, tools, motions). I don’t think there’s a better pair of people to hear from when it comes to building a modern marketing engine.

I went into this conversation expecting to hear about Claude Code workflows and fully agentic outbound systems. “More agents than humans” was the title I imagined I’d be giving this post.

Instead, I left with a thesis I didn’t expect: the marketing principles of the best teams are timeless, and the foundation matters more than the tooling. Collecting learnings is a job for humans, not AI. The perfectly built agent can’t scale something before a human—with taste and intuition—collects the context first.

There is too much focus on the tooling and what is new, when in reality, what really matters is the same set of foundational marketing principles. And, what will really matter as AI advances is not who has the best agent, but who can best use these new capabilities to tell incredible stories and move the minds and hearts of the market.

Their contrarian takes were refreshing. So, I was grateful they were willing to spend time sharing with me to put this piece together.

Alright, enough of my thoughts, let’s get to the golden nuggets of wisdom they dropped.

Alpha is in the timeless marketing principles of the last 100 years

There is no shortage of fomo on our feeds about how we’re all behind when it comes to using AI in go-to-market. I’m as guilty as anyone of this. Yet, Brandon’s main point in our conversation rings in my ears still:

The things that always unlocked alpha for me paired a deep understanding of the latest tactics with the timeless principles on human psychology and storytelling that have informed great marketing for the last 100 years.

And then the question becomes: “how much is the tactical landscape really changing versus the bar for execution on that same tactical playbook is going way, way up?” I would argue that the biggest shift is that the “permission to play” quality bar on the same set of tactics is rising every day. And over time, the returns will accrue to the teams that deeply understand how to tell incredible stories that resonate.

It’s not that you shouldn’t be using AI (obviously, you should). It’s just that AI doesn’t replace the timeless principles of marketing from the last century. AI raises the bar for execution.

Hire spiky mutants, not generalists

Brandon shared how he’s thinking about building a lean team at Netic, instead of scaling a marketing org to 100+ people (which was the playbook for almost every SaaS company until recently). AI changed that equation. He said (I love this line):

I’d rather have a small team of mutants who each are spiky in one or more areas than a larger team. Each person on the team should raise the bar on a different dimension.

Spiky means exceptional at one specific thing, not competent at everything. AI can automate a lot of the work that used to require many heads. Which means you want people who are amazing at a few things, and you can eventually build automation on top of their work. Some portion of the job is gone, and so you have to nail what’s left: taste, analytical skills, creativity, etc. (depending on function).

For Netic, that means five core pillars: Growth, Content, GTM Engineering, Events, and Product Marketing.

Fewer seats. Higher bar (and more scope) per seat. And a much clearer expectation that each person is a bar raiser on a specific dimension.

Where to search for Alpha (and where not to)

Part of being intelligent about GTM Engineering is knowing what dimensions you want to get alpha on and which ones you want to hold steady. Brandon framed it simply: where do you want to build your secret sauce, and where do you want to do no harm?

One example: Netic chose Salesforce as their CRM. People were surprised when Noah told them because he seems like the kind of operator who would go with one of the new AI-native CRMs (or just build it in-house). While an AI CRM might unlock some upside. It could also be brittle and break in ways that cost you more than the alpha is worth. Salesforce has the deepest integration ecosystem. It’s a known quantity. They chose predictable reliability at the infrastructure layer.

An example of a place they chose to find alpha: building out their TAM. Instead of pulling a standard firmographic list and calling it a day, they used creativity and thoughtful market context to go deep. They determined that using a new, innovative “source of truth” may not give them an edge. But creative workflows and applications on top of it would.

They’re still early in figuring out their tech stack, but current (other) tools include: Salesforce and Gong (as examples of getting the foundations right), and then Replit and Claude Code (as ways of building on top), and Clay (for things like lead orchestration, inbound enrichment, and signals).

I think this is one of the most useful mental models for any GTM leader right now. Every week, there’s a new tool or tactic on LinkedIn or X that feels urgent. Brandon pointed out that the best operators have the judgment to know which bets compound and which ones are distractions.

SDRs aren’t dead; dumb SDRs are dead

Netic has two SDRs right now. Both are senior; one was previously a manager of a 15-person team and is now back in the trenches. Both understand the space deeply (deep customer empathy).

What’s different from a traditional SDR setup is how tight the feedback loop is. Every week, Noah runs what they call a “Shark Tank meeting” with the SDRs. He surfaces signals, they align on account selection criteria, and then they review emails being sent, live conversations happening, and responses. That feedback gets baked back into the following week’s account selection, signals, and messaging.

It’s a very deliberate system where the SDRs are generating context, pattern-matching, and building the playbook that the Growth/GTM Engineers (GTMEs) will codify over time.

Here’s a quote from Noah’s recent piece (the entire thing is worth reading) on his terrific newsletter, the gtm engineer:

Even if we thought we could use AI to automate everything Grif and Aidan are doing (and we don’t), the priority is learning whether outbound works

One specific example Noah shared: when an SDR comes across two CEOs—one with no digital presence and the other with one—they know to handle the outreach to those two CEOs completely differently. That intuition is something you can’t templatize on day one. Only humans can quickly gather that context.

Earn the right to automate

This was the throughline of the entire conversation.

Brandon told me: “The best alpha comes when you’ve built deep intuition on your market, and already have a sense of what works.”

He argues that one of the biggest mistakes a company like Netic could make right now is rushing to automate every workflow before they’ve actually built an understanding of what works. They’re not anti-automation. They’re just sequencing it correctly. The SDRs are accumulating context. When the GTME comes on board, they’ll have access to all of that accumulated knowledge, so they can automate the right things instead of guessing.

Noah made the case for why this works better with dedicated humans. He said (and this is coming from someone running one of the biggest GTME podcasts/newsletters in the world): “You get the context way faster when you have humans full-time thinking about it, versus a GTME who’s splitting time across eight other projects.”

This is a pattern I keep seeing at the best companies. The ones who automate well are the ones who did it manually first, learned what actually worked, and then built systems around the proven playbook. The ones who automate poorly are the ones who skipped the manual phase entirely. One note: for orgs that are farther along, the manual work may have already been done, and you can drive meaningful alpha by finding it and automating. So, if a company has 1,000 sign-ups per month, CS needs help. At that point, you would “watch the process + automate” (since you’re already doing it manually).

Noah also said: “Go prove the ceiling is sufficiently high before you think about all the automation.” If something doesn’t work manually, automating that thing definitely won’t work.

For example, if you go look at your inbox right now, you’ll see that most outbound emails are still bad. The reason is that people took the C+ or B- version and scaled the hell out of it with AI. They automated mediocrity. Noah’s challenge: Have you sent a single email (even one!) manually that has gotten a positive reply? If the answer is no, why are you trying to send 100,000 emails a month?

You can still cut through the noise. You just have to do it well. The teams that find the A+ version, write the emails manually, prove the message works, and then automate it—those are the ones that win. Everyone else is producing more noise at higher volume.

Brandon took it further: “I would take a totally analog GTM hire/org that produces hyper-relevant things over a fully automated one that isn’t working. AI is a means to an end. You don’t want your GTM org to be known for being AI-pilled. You want it to be known for producing awesome, valuable things.”

They both are using AI. Just as a means to an end.

Every Exec needs to be in these (AI) tools

Brandon went from leading a ~200-person marketing org at Rippling to personally coding things himself at Netic. Building marketing assets. Getting inside the tools. His view is that leaders of the future need to be immersing themselves in this stuff, because the way you execute the same types of tactics is changing, even if the tactics themselves aren’t.

The current environment has created more noise than ever before, more competitors, more emails, more everything. And AI will continue to exacerbate this new reality.

Here’s what Brandon said about the current moment:

The playbook is actually a lot of the same things, like events, signals, driving engagements, etc. But AI slop has made it such that the way you need to execute it: the need for authenticity and caliber is way more important.

Execs (including Brandon) are getting their hands dirty. Building things that, two years ago, would have been delegated three levels down. Now, you can (and should) build these things yourself—all of the best executives and founders are hands-on right now.

The playbook is broadly the same. The channels, the motions, reaching the right company at the right time with the right message. It’s just that the bar for execution is much higher. And leaders who aren’t operating inside the tools are going to fall behind.

There’s a lot of noise on LinkedIn right now about reinventing GTM from scratch. New playbooks. New roles. New everything.

Brandon and Noah are building from scratch at Netic with every advantage you could ask for: Rippling-scale experience, backed by Tier 1 investors, and a market (service enterprises) that’s massively underserved by modern GTM tooling. And their takeaway after six years of scaling one of the fastest-growing B2B companies in the world?

If you’re building a GTM org right now, the question isn’t whether to use AI. Of course, you should. The question is whether you’ve earned the right to automate, whether you know what works before you scale it, and how to use AI to accelerate certain JTBD if/when helpful.

Picking winners

Noah also pointed out that winning in GTM is about picking the right company. You can come up with all these crazy AI go-to-market tactics, but the most important thing is whether people really like the product. And the best sign of PMF is revenue traction without anything built out in GTM yet. Brandon added: “I think GTM magic happens when you pair an incredible product and market and founder with top-notch GTM creativity and execution.” That’s Netic.

So if you are looking for a winner, check out Netic. They’re hiring across Growth, Content, and GTM Engineering. If you want to be part of a world-class GTM org selling a product with breakout PMF—an engine with humans at the center, where you’ll be asked to build “jaw-breaking” outbound experiences—reach out to Noah.

PS

Here is the original interview clip (8 mins) that caught my attention. If you read this far and want to hear more from Noah and Brandon directly, it’s 100% worth watching:

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Topline was kind enough to ask me to write a guest post for their newsletter (125K readers). You can check it out here:

Topline

GTM Engineer: The Most Misunderstood Role in Go-to-Market by Brendan Short

Every revenue leader is trying to double their number without doubling headcount. AI is the obvious lever. The less obvious question: who owns building AI-native connective tissue across the GTM org…

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3 months ago · 141 likes · 2 comments · Topline and Brendan J Short

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“AI SDRs don’t work.”

That’s what Prabhav Jain (CEO of 11x, one of the most well-funded companies in the AI SDR space, backed by a16z and Benchmark) told me. The company that helped create the category is telling you the category label is wrong.

I wanted to talk to Prabhav because I’ve been living in this world for a long time. I co-founded one of the first AI SDR companies (Hexa.ai) back in 2016. We positioned ourselves as “replacing SDRs with AI.” Which is funny because 1) AI was barely a thing back then, and 2) I think “AI SDR” is bad positioning. It got acquired by OutboundWorks in 2018, which was autonomously booking meetings on behalf of AEs. Years later, I spent time at Apollo running outbound (internally titled “AutoSDR”) for 20 AEs and no SDRs. So I’ve been building, buying, and deploying this stuff from multiple angles for over a decade.

And I’ve had some version of the same conversation about 50 times in the last six months. A CRO or VP of Sales tells me they’ve been told to “figure out AI.” Their CEO or board wants them to triple pipeline without adding headcount.

So they start taking calls from AI SDR vendors. Every vendor says the same thing. Every demo looks great. And then three months into a contract, half of them are wondering what happened.

The problem isn’t AI. The problem is how teams are deploying it.

Prabhav is the CEO of 11x. Before 11x, he was Head of Engineering at Brex, has founded two companies, and invested in another 50. He’s a builder. And the conversation reflected that.

This post is a sponsored deep dive distilling that conversation, combined with patterns I’m seeing across my own experience over the last 18 months.

In this post, we cover:

• Why the “AI SDR” label is the problem

• The question every CRO should answer before buying anything

• The bake-off trap (and how to avoid it)

• How to practically deploy AI agents

• Your data is terrible, and agents can actually fix it

• The inbound gap nobody is talking about

• A peek into the future

• What the best teams look like in 12 months

Alright, let’s get into it.

Why the “AI SDR” label is the problem

The term “AI SDR” sets the wrong expectations from day one. People hear it and immediately assume they’re going to plug this in, replace a headcount, and start getting meetings tomorrow.

That’s not how any of this works. And Prabhav would know. His company helped define the space.

Think about your best sales rep. Were they your best performer in week one? Of course not. They ramped. They learned your ICP, your messaging, how to handle common objections, etc. They experimented. They failed. They got better.

AI is the same. The difference is that once it ramps, it can run 11 experiments simultaneously instead of one. It can test messaging variants, channel sequences, timing, and personalization at a scale no human team can match. The compounding effect is real. But it compounds from a starting point, and that starting point requires patience and clear thinking.

The companies getting results aren’t the ones that plugged in a tool and walked away. They’re the ones who treated AI deployment like onboarding a new team member. They were willing to experiment and didn’t expect success from every iteration. Dedicated time, clear goals, and a willingness to iterate daily, especially in the early days of an AI deployment.

The question every CRO should answer before buying anything

Before you evaluate a single vendor, answer this question. What is the specific bottleneck in my pipeline that, if removed, would produce more revenue?

If you can’t answer that in one sentence, you’re not ready to buy.

For some teams, the answer is obvious. “We aren’t getting enough meetings booked from cold outreach.” Okay, that’s a top-of-funnel automation play. For others, it’s: “We have plenty of inbound leads, but our speed-to-lead is terrible, and half of them go cold.” That’s a completely different problem with a completely different solution.

Without that diagnosis, every AI vendor looks like a fit. Because every AI vendor says they do everything.

Prabhav told me the most sophisticated buyers he works with at Fortune 1000 companies show up to the first call with an IT admin, a data scientist, a security person, and a CIO-type. They’ve already mapped the problem. They know exactly what they want automated and what they want to keep in-house. It’s a completely different buying motion than what most mid-market companies are doing, which is more like: “AI seems important, let’s try something.”

The best framework is to work backwards from the metric you’re trying to move. Meetings booked? That’s outbound automation. Conversion rate on inbound? That’s speed-to-lead and qualification. Pipeline quality? That’s better lead scoring and research before anyone reaches out.

AI is a tool. Like any tool, it works when applied to a specific problem. It fails when applied to a vague ambition.

The bake-off trap (and how to avoid it)

I see this mistake constantly. A team decides to “test AI,” so they sign up for three vendors simultaneously, give each one a slice of their territory, and run a 90-day bake-off.

Sounds rigorous. Almost always fails.

Why? Because nobody owns it. The SDR leader is still running their team and is often threatened, so they want the vendor to fail. The ops person is juggling five other projects. Nobody has dedicated time to configure each tool, monitor results, give feedback, and iterate on the messaging. Each vendor gets a half-hearted deployment, produces half-hearted results, there is little or no iteration, and the team concludes that “AI doesn’t work for us.”

The fix is simple but requires discipline. Pick one vendor. Assign one internal owner who has real time to dedicate (not 10% of someone’s plate). Define the KPIs before you start. Make the vendor demo actual workflows live, not slide decks (if they won’t show you the platform, that’s a red flag). And commit to 90 days of daily iteration before judging results.

The companies Prabhav described as their most successful customers all had one thing in common. They had a dedicated internal owner who treated the deployment like a real project (or a new, full-time hire), not a side experiment.

How to practically deploy AI agents

There’s a false binary in the market right now. People talk about it like you have two choices: buy an off-the-shelf AI SDR product, or build your own agents in-house.

The reality is that most companies end up doing both, and the split depends on your size and sophistication. It’s not “Build or Buy,” it’s “Build and Buy.”

For example, a company may want to keep lead scoring/prioritization and their knowledge base in-house. These are critical components of a GTM machine.

Then the question becomes: where do you want to build+deploy AI agents yourself?

11x has a unique take on this: what if you rebuilt the orchestration layer from scratch, knowing what agents can do today? They want to build the best possible versions of each building block (lead finding, enrichment, scoring, research, message writing, CRM integration, phone, email, chat, SMS) and then let you compose them into whatever workflow you need. Think of it as providing the infrastructure so you can say “anytime someone from my target list visits my website and spends 30 seconds on the pricing page, send me a Slack notification and trigger a multi-channel sequence.” You bring the ICP logic and the knowledge. Their platform provides the execution.

(I’ve written about this tension before in The $100B+ Winner in GTM Tech Will Sell Labor, Not Software. The thesis is the same: winners in GTM tech are going to sell outcomes, not seats.)

Your data is terrible, and agents can actually fix it

This is one of the most underrated dynamics I’ve seen.

I talk to revenue leaders regularly who are scared to let AI agents touch their CRM. And honestly? They should be. The data in most CRMs is terrible. Duplicate records, outdated titles, wrong email addresses, and contacts who left the company two years ago. If you unleash an agent on bad data, you get bad outreach at scale. Which is worse than no outreach at all.

Most people don’t expect this, but agents can actually fix the data they run on.

When an AI agent runs an outreach campaign, it generates data exhaust. Bounced emails tell you the contact is outdated. Response data tells you which titles and personas actually engage. CRM activity logs get updated automatically because the agent is doing the work, not a human who forgets to log it. Over time, the data gets cleaner, the targeting gets sharper, and the results compound.

It’s a positive feedback loop. The agent cleans the data it operates on, which makes the agent more effective, which generates more exhaust, which cleans more data.

This doesn’t happen on day one. Which goes back to the point about iteration. The teams that give AI 90 days to ramp and iterate see this flywheel kick in. The teams that judge results after two weeks never get there.

The inbound gap nobody is talking about

Everyone’s focused on AI SDRs in outbound, which makes sense. Outbound is where the category started, and it’s where most of the marketing dollars go. It’s the easiest to quantify.

But for companies with existing inbound volume, the fastest path to more pipeline is converting what’s already coming through the door.

Think about what actually happens at most companies. A prospect visits your website. Maybe they engage with a chatbot. Maybe they don’t. Maybe they fill out a “Contact Sales” form. What happens next?

In most companies, an SDR checks their queue a few hours later and sends an email. By then, the prospect has already moved on. They’ve visited three other websites. They’ve forgotten why they filled out that form.

The data on speed-to-lead has been around for over a decade. Responding in the first five minutes gives you roughly an 8x higher chance of qualifying a lead compared to responding in 30 minutes. And yet the average B2B response time is still measured in hours.

What Prabhav walked me through on our call was a different approach entirely. When he thinks about inbound, he doesn’t start with “we have a chatbot.” He starts with: “If your sole job was to convert every person visiting your website into pipeline, what would you actually have to do?”

The answer is you’d have to be multi-channel by default. A prospect fills out a form. You call them in under 20 seconds (that’s what 11x’s Julian does). They don’t pick up? Call again. Still nothing? Text them. They respond over text? Qualify them right there. They don’t respond? Email them. The system adapts to whatever channel the prospect prefers.

That’s fundamentally different from “we have a chatbot on our website.” And it’s the kind of orchestration that’s nearly impossible to duct-tape together with a bunch of API calls.

I think inbound orchestration is the most obvious, highest-ROI AI use case in GTM right now, and it’s barely being discussed. If you have inbound volume and you’re not responding in seconds across multiple channels, you’re leaving pipeline on the table every single day.

A peek into the future

Right now, agents are very good at execution. You have an idea, and the agent can go out and make that campaign happen.

What if the agents were a strategist that you could partner with? Agents that actively help you define campaigns based on what’s working and what’s not, the previous activities you’ve had for your key target accounts, ingesting all that data to help you prioritize and run the different plays that actually result in more pipeline and more revenue. Agents that stop you from launching a weak campaign.

This unlock, in terms of the quality of models and type of reasoning they can do, was only really made possible this year, and that future is coming very shortly.

What the best teams look like in 12 months

Many of the best GTM teams a year from now won’t look like today’s teams with AI bolted on. They’ll be structured differently.

AI handles the initial research, the multi-channel outreach, the follow-up sequences, and the inbound qualification. Humans handle closing, relationships, and the complex deals that require judgment, empathy, and creative problem-solving. The stuff that makes sales a craft, not a coin-operated machine.

This matters because the companies that try to fully replace their teams with AI tend to end up worse off than where they started. The technology isn’t there yet for full autonomy on complex sales cycles. But it’s absolutely there for handling the 70% of an SDR’s day that isn’t actually spending time with prospects. The research, the data entry, the follow-up scheduling, and the manual outreach across channels. Especially in lower ACV segments (as outlined in the 2x2 matrix I shared here: “Should You Buy an AI SDR?”).

The gap between teams that deploy AI thoughtfully and teams that are still “evaluating options” is compounding every week. If you’re a CRO, GTM leader, or founder reading this, start with the diagnosis. What’s your pipeline bottleneck? Answer that, and the rest of the decisions get a lot easier.

Reach out to 11x if you want help deploying digital workers, and let them know The Signal sent you.

Thank you for your continued attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

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Presented by Clay.

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps

Clarify → The Autonomous CRM

Nooks → Agent Workspace for Intelligent Outbound

Sumble → Buying signals from tech stacks, people data, & active projects

11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

Outbound has been around for, well, a long time. While LinkedIn has called it dead every quarter for the last 24 quarters, it feels fresh again because of two forces, data and automation.

There’s so much better data on both who your target customer is and who’s ready to buy. When asked which GTM channels B2B companies are investing the most into for 2026, intent-based outbound was #2 on the list and warm outbound took the #5 spot. Buyers may hate AI slop cannons, but (good) outbound still pays the bills.

In February 2025, Kyle and I published “The Best Automated GTM Plays You’re Not Running.” It was a menu of 25 plays like website visitor outbound, champion tracking, closed-lost re-engagement, and micro-campaigns. That post still holds up. The plays are still good. But the way you build them has changed… a lot.

14 months ago, “automation” meant setting up a signal that gets triggered, enriching a list in Clay, and having AI attempt to write an email. Today, it means AI agents that monitor signals continuously, pull research from your CRM and the web in real-time, and draft outreach that references what the prospect actually said on their last call with you.

Four things shifted:

1. AI has a better context layer that understands your business.

2. AI agents now execute multi-step workflows autonomously.

3. MCP (Model Context Protocol) connects AI directly to your GTM stack.

4. The best teams stopped thinking in “plays” and started thinking in “systems.”

So we’re back. Instead of 25 plays at a surface level, we picked 5 workflows to show you how AI has changed how you set up GTM systems in 2026.

Important to notice: these workflows are a combination of deterministic and agentic steps.

Alright, let’s get into it.

Play 1: Closed-Lost Deal Re-Engagement

Original version (2025): Set a CRM automation to flag opportunities closed-lost 9 months ago. Rep reviews, writes a re-engagement email. Maybe they use the OpenAI API to summarize the last call and insert that summary into the email with a line like: “To jog your memory, here’s what we talked about last time.”

That was a solid play, but it had two problems. The timing was arbitrary. Why 9 months? What if the right moment to re-engage was 4 months (because they got a new VP) or 14 months (because they just raised a round)? And the summary was generic. It told you what was discussed, but not why the deal died.

The AI-native version: An agent monitors your closed-lost pipeline continuously.

The trigger is a cluster of re-engagement signals at the account: leadership change, new funding, job posting for a relevant role, champion who killed the deal left the company. When enough signals stack, the agent fires.

When it fires, the agent pulls the call transcript from the last conversation. It identifies the specific objection that killed the deal. “They went with [competitor] because of [specific feature gap].” Or “budget got pulled because of [reorg].”

Then it drafts an email that references the objection and what’s changed. For Tier 1 accounts, the email routes to the rep for review. For Tier 2 and below, it sends automatically.

The key shift: Timing is dynamic instead of calendar-based. And the email references the real reason the deal died, not a generic recap.

Tool chain: CRM (closed-lost pipeline) → Signal sources (job changes, funding, leadership) → Attention.com or call transcript via API and/or email replies (transcript + objection extraction) → Clay (enrichment + orchestration) → Claude (email drafting) → Sales Engagement Platform, CRM, or Slack.

Pro-tip: Stack this with champion tracking. If the person who killed your deal left the company, that’s a completely different play than if they’re still there. Your agent should know the difference and adjust.

Play 2: Micro-Campaigns via AI Agents

Original version (2025): Run outbound campaigns aimed at small, highly-targeted lists of 50-250 contacts with very specific filtering and signals. Each list is relevant this week, but won’t be next month. We gave an example: a company with a database of pre-vetted sales candidates scans target accounts daily, and when any post an AE job, reaches out to the VP of Sales with three candidates.

The concept was right. The execution was still mostly manual. Someone had to decide when a micro-campaign should exist, build the list, write the copy, and launch it. That took hours per campaign.

The AI-native version: The agent decides when a micro-campaign should exist and surfaces it themselves.

You define your ICP and your signal sources (job postings via Sumble, G2 reviews, LinkedIn engagement, first-party data from CRM/MAP/DW, etc.). The agent scans your target account list continuously.

When a signal cluster fires, meaning multiple signals stack on the same account or set of accounts within a short window, the agent:

1. Builds the micro-list automatically (50-100 contacts, ICP-scored)

2. Enriches each contact with relevant context (LinkedIn posts, role tenure, tech stack, mutual connections)

3. Generates campaign-specific copy, i.e. actual copy written for this specific micro-moment

4. Stages the campaign for review or auto-sends based on a confidence threshold

A concrete example: You sell a data integration tool. Your agent detects that 13 companies posted 2 or more Data Engineer roles mentioning Fivetran (a competitor), and a Key Buyer either a) liked a LinkedIn post about migrating off Fivetran or b) found this in call transcripts in your CRM. Three signals stacked, finding 13 companies. The agent auto-prospects the right buyers at these 13 companies and compiles a micro-list, writes copy referencing the migration signal, and stages it. That campaign didn’t exist last week and won’t be relevant in 3 weeks.

The key shift: Micro-campaigns used to require a human to spot the pattern and build the campaign. Now the agent spots the pattern, builds the campaign, and presents it to you for approval (or just runs it).

Tool chain: Signal sources (Sumble, G2) → Clay (orchestration + enrichment + ICP scoring) → Claude (copy generation) → SEP

Pro-tip: Start with one signal source and one ICP segment. Go with something highly specialized that’s unlikely to be immediately copied. Get the agent working on a narrow slice before expanding.

Play 3: Champion Tracking + Warm Intro Combo

Original version (2025): Some of the best performing GTM plays are where there’s a pre-existing relationship that can be leveraged like a past champion or a mutual investor relationship. We listed three separate relationship-based plays: past champions change jobs, existing champion’s prior company is ICP-fit, and mutual investor or advisor intro.

Each play had its own trigger, tool setup, and manual triage. Three separate workflows and three separate lists to maintain.

The AI-native version: Multiple plays triggered from the same system using a single agent and relationship graph.

The agent builds and continuously updates a relationship graph that maps your warm network: current champions, past champions, their job changes, their prior companies (scored for ICP fit), mutual investors and advisors, and LinkedIn engagement patterns (who’s engaging with your content or your competitors’ content).

When any node in this graph changes, the agent evaluates all possible warm paths into target accounts and ranks them by likelihood of success.

A few scenarios:

• Champion changes jobs: The agent detects the move, checks if the new company is ICP-fit, pulls context on the company (size, stack, funding), and drafts a congrats + re-engage email. If the new company is already in your pipeline, it alerts the rep and suggests the champion as a warm path in.

• Champion’s prior company is ICP-fit: The agent identifies the connection, checks if you have an active opportunity there, and drafts a warm intro request for the champion. The ask is specific: “Would you be open to introducing me to [specific person] at [prior company]? We think [product] could help them with [specific use case].”

• Mutual investor connection: The agent maps your investor portfolio against target accounts, finds overlaps, and drafts intro requests through the investor. Ranked by relationship strength and deal stage.

The key shift: You’re running a single system that continuously evaluates the best path into every target account. The agent picks the play. You (or your rep) execute it.

Tool chain: Sumble, UserGems, Clay, etc. (job change tracking) → Clay (enrichment + ICP scoring) → The Swarm (relationship graph) → Claude (strength reasoning + outreach drafting) → SEP or Slack alert.

Pro-tip: The most underused warm path is “champion got promoted at the same company.” They now have more budget authority and more influence. Your agent should treat a promotion the same way it treats a job change: as a trigger to re-engage with a bigger ask.

Play 4: Competitor Displacement via Tech Stack Signals

Original version (2025): Tech stack data makes for a great signal. We previously recommended using data scraped from job postings to find companies using a competitor or complementary product. Traditional technographic providers scrape websites, which works for tools with a web pixel. For everything else, job postings are the signal (because a Data Engineer role will mention “proficiency in our stack, which includes Snowflake”).

That was a good start. But the play was essentially: build a list of competitor users → send competitive messaging. The messaging was usually based on your marketing team’s battlecard, not on anything specific to the prospect.

The AI-native version: The agent monitors multiple displacement signal sources continuously:

• Job postings mentioning competitor tools (via Sumble)

• G2 and Capterra reviews, especially negative ones, from companies in your ICP

• LinkedIn activity from prospects engaging with competitor content (commenting, liking, sharing)

• Hiring patterns that suggest a stack change (e.g., a company hiring for a tool they don’t currently use, which signals they’re evaluating a migration)

Any single signal is interesting. When signals stack (negative G2 review + new hire + job posting dropping a competitor from the required skills), that’s a displacement opportunity.

When the agent detects a stacked signal, it:

1. Identifies the right contacts: current users of the competitor + the decision maker who controls the budget

2. Pulls specific pain points from reviews and LinkedIn posts (AI-summarized)

3. Maps those pain points to your product’s specific differentiators

4. Generates outreach that references their frustrations, not your static battlecard

A concrete example: Your agent finds a G2 review from an ops manager at a target account saying their current tool “breaks every time we try to sync more than 10K records.” A week later, the company posts a job for a RevOps lead mentioning experience with your product category. The agent builds a 5-person list, and the email to the ops manager opens with a reference to the scaling challenge (without quoting the review verbatim, obviously) and links to a case study from a company that migrated to you and solved that exact problem.

The key shift: The messaging is built from what the prospect’s team is actually saying about the competitor, not from your internal positioning doc.

Tool chain: Sumble (tech stack signals from job postings) → G2/Capterra (review monitoring) → Clay (orchestration + enrichment) → Claude (pain point mapping + outreach generation) → SEP

Pro-tip: The most effective displacement emails lead with the prospect’s specific pain and offer a path forward. “I noticed your team is scaling fast and running into [specific challenge pulled from OKR/goal on the job description]. Here’s how [similar company] handled that transition...” works better than “Ready to switch from [competitor]?”

Play 5: AI-Generated Prospect Research Brief + Meeting Deck

Original version (2025): You can usually afford to invest extra effort in personalization for your best Tier 1 prospects. We previously recommended pulling together a research brief before a first call: recent news, competitive intel, relevant context.

In practice, most reps skipped this step. It took 20-30 minutes of manual research per call. The ones who did it were disproportionately effective, but the majority showed up underprepared.

The AI-native version: 30 minutes before every first call (triggered by the calendar event), an agent auto-generates two deliverables:

Deliverable 1: A 1-page research brief for the rep. This includes:

• Company snapshot: funding stage, headcount trajectory, recent news, tech stack, key competitors

• Contact snapshot: role tenure, LinkedIn activity (recent posts, comments), past companies, mutual connections

• Competitive context: what tools they use in your category, any recent changes or reviews

• Conversation starters: 3 specific talking points based on the prospect’s recent LinkedIn activity or company news

• Objection prep: based on their company profile (stage, industry, stack), the 2 most likely objections and how to handle them

Deliverable 2: A personalized meeting deck (3-5 slides). This is not your standard pitch deck. The agent generates a short deck tailored to this specific prospect:

• Slide 1: Their company’s situation

• Slide 2: The challenge you believe they’re facing

• Slide 3: How you solve it

• Slide 4: A relevant case study

• Slide 5: Suggested next steps

Both deliverables are pushed to the rep via Slack or email, 30 minutes before the call. They open the brief, skim the deck, and walk into the meeting with more context than 90% of reps who “did their research.”

The key shift: Every rep now shows up as prepared as your best rep. The floor rises across the entire team. And the prospect gets a deck that actually reflects their world.

Tool chain: Google Calendar (trigger) → Clay (company + contact enrichment) → Claude (brief generation + deck generation) → Google Slides API or PDF generation → Slack/Email delivery

Pro-tip: After the call, feed the call transcript back into the agent. Have it update the brief with what was actually discussed, what objections came up, and what the next steps are. Now you have a living account brief that gets smarter after every interaction.

Where this is going

14 months ago, these were plays you built. Today, they’re systems that build themselves.

I think in another 12 months, most growth-stage GTM teams will have 5+ of these agent workflows running continuously. Automated GTM plays will become core revenue infrastructure. The closed-lost agent will be as standard as a drip campaign. The micro-campaign agent will replace the Monday morning “what should we target this week” meeting.

The teams that build these systems now will compound their advantage every month. The teams that wait will feel it in their pipeline, their win rates, and their rep productivity.

If you found a play here worth building, start small. Pick one. Get it working on a narrow segment. Then expand. And if you build something creative that we didn’t cover, tell us. We want to know.

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Related posts:

• 9 Lessons From 11 Growth-Stage Companies That Built GTM Agents In-House (Vercel, Ramp, LangChain, ClickUp, Deel, Vanta & others: what they built, their tech stacks, and what we can learn from them)

• 6 Ways GTM Teams are using MCPs and Claude/Claude Code Today (+ Video demos of real examples from yesterday’s live event)

• 6 Ways to Modernize Your GTM Motion in 2026 (Lessons from working with 14 Seed-Series C companies in 2025)

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Save My Seat

Hey y’all! 👋

Happy last day of the quarter! (to those who celebrate fiscal Q1, anyway.) Get those last gong hits in.

I was talking to a CRO at a fast-growing startup recently, and we were both shocked by how badly people misunderstand what you can actually do with client interaction data (call transcripts, emails, Slack messages, etc.).

Most teams treat their conversation intelligence platform like YouTube. A library of recordings you go back and watch when you need to find information or build a new onboarding program. Maybe you pull up a call before a QBR. Maybe a manager listens to a few calls a week for coaching. But the platform mostly just... sits there.

Your call recordings aren’t a content library. They’re some of your most actionable first-party data. Every call contains buyer language, objections, competitor mentions, champion signals, pricing reactions, feature requests, and expansion cues. The best sales orgs are building hundreds of agents and workflows triggered by stuff that gets mentioned on their calls.

Most people know they should be doing more with AI and their customer interactions. But they aren’t. They’re still just doing summaries, recap email drafting, and some manual coaching.

There’s a real opportunity to use platforms, like Attention.com, to move from passive call recorders to proactive AI agents that turn every conversation into action.

Last April, I wrote a deep dive on Attention.com: The Army of AI Agents That Turn Your Sales Conversations into Action. Here’s how I opened that piece:

Most people building go-to-market tooling are obsessing over third-party data. But, there is a goldmine of information in every company’s systems already: customer conversations, emails, and meeting transcripts.

The problem is - this data sits dormant (becoming less useful over time as it collects dust in the corners of the CRM or CDW or otherwise), and it is distributed across disparate systems and “objects.” Attention is activating this data. Mining for the interesting nuggets and then operationalizing them, in real-time. That’s the vision they’re realizing, by building a system of AI agents that don’t just capture sales conversations, they automate the work traditionally done by the best enablement analysts, RevOps specialists, and top performers.

That was 12 months ago. It’s even more true today.

But AI is only half the unlock. The other half is knowing which plays to run.

So, for today’s sponsored deep dive post (with Attention.com), we put together 15 core plays that the best GTM teams are running on client interaction data right now. These can be done with Attention.com out of the box. But, even if you aren’t using them, you can set up these plays.

Steal them, and make them your own. All of them are powered by the same raw material: the client interactions you already have captured.

Alright, let’s jump in.

Sales (AEs & SDRs) Plays

1. Auto-populate MEDDIC fields after every call.

The transcript gets parsed and Metrics, Economic Buyer, Decision Criteria, Decision Process, Identified Pain, and Champion get written directly into CRM fields. Not with generic placeholders, with the actual language from the call. “Their CFO needs to see a 3x ROI within 12 months” goes straight into Metrics. “VP of Ops, Sarah Chen, is our internal champion but reports to a skeptical CTO” populates both Champion and Economic Buyer.

This saves reps 20–30 minutes per meeting. But the bigger win is accuracy. Reps filling MEDDIC fields from memory at 5pm on a Friday are guessing. AI parsing the raw transcript with a truth-seeking prompt is not. One team saw their MEDDIC completion rate go from 35% to 92% within three weeks of turning this on.

2. Draft follow-up emails using the prospect’s own words.

Within 60 seconds of a call ending, AI generates a personalized follow-up pulling the buyer’s exact challenges and action items. Not your marketing team’s words, or your words, the prospect’s words.

When a VP of Sales tells you “our biggest problem is that reps spend more time in Salesforce than on the phone,” and your follow-up email opens with that exact phrase, you signal that you actually listened. Teams report 23% higher response rates than manual follow-ups. That tracks. Compare this to the generic “Great connecting today! As discussed, here are some next steps...” emails that most reps send. The prospect said specific words. Use their exact language in your follow-up.

3. Surface battlecards mid-call in real time.

When a prospect mentions a competitor, raises a pricing objection, or asks a technical question, the right talking points hit the rep’s screen while the conversation is still happening. AI-powered battlecards help you answer any prospect question while on the call.

Think about how this changes the dynamic. A prospect says “we’re also looking at [Competitor X],” and within seconds the rep has the top three differentiators against that competitor and the most effective rebuttal from top performers, all on screen. Less “let me get back to you on that question” and more “here’s exactly how we handle that.” This is especially powerful for newer reps who haven’t built up the pattern recognition that a 10-year AE carries in their head.

4. Auto-generate business cases from multi-call synthesis.

After 3–5 conversations, AI compiles every stated goal, ROI expectation, and buying criterion into a business case draft using the metrics discussed on the calls. No more building decks from memory.

The keyword is synthesis. The prospect’s CEO mentioned “reducing time-to-hire by 40%” on call one. The VP of Ops talked about “cutting onboarding from 6 weeks to 2” on call three. The CFO said in an email “we need to justify any new spend against a 14-month payback period.” AI pulls all of those threads together into a single business case doc that mirrors the buyer’s own success criteria, not your generic ROI calculator. This turns a 4-hour deck-building exercise into a 15-minute review.

5. Build stakeholder maps from every transcript mention.

Every person mentioned by name or role across all deal calls gets mapped, including people who never joined a meeting. “I’ll need to run this by our legal team” on one call, and then “my manager, David, is going to want to see a security review” on the next call. Those passing mentions build a full picture of the buying committee.

Closed-won opps have 2x more buyer contacts than closed-lost opps. Most reps only map the people who show up on a Zoom. The stakeholders that get mentioned but never join are often the blockers or the final approvers. If your deal has a ghost stakeholder you’ve never engaged, that’s a risk. This play surfaces them before it’s too late.

6. Detect single-threaded deals and trigger multi-threading.

Buyer-side participant count gets monitored per deal, and an alert is triggered when a deal is single-threaded. The system tracks how many unique buyer-side voices appear across calls, how many distinct email addresses are engaged, and whether new stakeholders are being introduced over time.

When a deal has only one contact after three calls, it gets flagged automatically with a suggested action: “Consider requesting an intro to [role] based on similar closed-won deals.” Multi-threading boosts win rates by 130% in deals over $50K. But most teams don’t catch single-threaded deals until the forecast review (when it’s too late). Running this as an automated trigger means the rep gets the nudge in week two, not week eight.

7. Alert on deal velocity drops from engagement patterns.

Each deal’s email cadence, call frequency, and response times get benchmarked against closed-won norms. When engagement drops below the healthy baseline, the system fires an alert before the deal goes dark.

It’s not just “this deal is slow.” It’s specific. “This deal averaged 3.2 emails/week for the first 4 weeks. Over the last 10 days, it’s dropped to 0.4 emails/week. Similar deals that followed this pattern closed-lost 78% of the time.” That gives the manager something concrete to coach on in the next 1:1. Closed-won deals average 8.2 emails per week vs. 1.9 for closed-lost. The velocity data is already in your system. You just need to benchmark it and set the threshold.

8. Build personalized objection heat maps per rep.

I wish I had this when I was managing reps. Here’s how it works: every objection each rep faces gets categorized and cross-referenced with outcomes. Rep A converts pricing objections at 45% but timing objections at 12%? Now you know where to coach.

Instead of generic team-wide training on “handling objections,” managers can pull up a rep’s individual heat map and say: “You’re great on pricing and competitive pushback. But you’ve faced 14 timing objections in the last quarter and converted only one. Let’s listen to how [top performer] handled a similar timing objection on the [xyz] deal.” You can scale this across a 30-person team, and you’re running personalized enablement without adding headcount.

RevOps Plays

9. Score deal risk from conversation signals, not CRM stages.

Health scores get assigned based on what’s actually happening in calls: Economic Buyer participation, objection resolution, sentiment trends, not just the stage a rep selected. Attention.com’s reporting and forecasting module does this automatically.

Think about how most deal health scores work today. A rep moves a deal to “Proposal Sent” and the CRM says it’s 60% likely to close. But what if the last two calls had declining sentiment? What if the Economic Buyer hasn’t joined a call since discovery? What if the prospect asked “can you send over pricing so I can compare it to [Competitor]” (a comparison signal, not a buying signal)? Conversation-based scoring catches all of this. One team found that deals where the Economic Buyer appeared on fewer than 30% of calls closed at half the rate, regardless of CRM stage.

10. Validate pipeline forecasts against conversation evidence.

What reps claim in the CRM gets cross-referenced with what was said in calls. A rep marks “Commit” but the prospect hasn’t responded in 10 days? Flagged. A rep calls it “Best Case” but the last call had three unresolved objections and no next meeting scheduled? Also flagged.

This is sandbagging and happy-ears detection at scale. Most forecast accuracy issues come from the same problem: the forecast is based on what the rep believes, not what the prospect said. Cross-referencing CRM stage against transcript evidence closes that gap. A lot of sandbagging AEs are about to get exposed (looking at you, Tyler). But so are the optimistic ones who genuinely believe a deal is on track when the transcript says otherwise (ahem… Nick). RevOps leaders report 15–20% improvement in forecast accuracy within the first quarter of running this play (the board is going to love you for implementing this one).

11. Automate win/loss analysis across every closed deal.

Every call and email for closed-won and closed-lost deals gets reviewed. Surfacing which objections, competitors, personas, and messaging patterns drove outcomes. The day the deal closes, not the next quarter.

Traditional win/loss analysis is a quarterly project. Someone interviews 10–15 accounts, writes up themes, and presents findings 6 weeks later. By then, the market has moved. With AI, every closed deal gets analyzed automatically: which competitor was mentioned most, which objection went unresolved, whether the champion went silent before close-lost. Attention.com’s Ask Anything feature lets you query across all calls to understand why you’re winning and losing. “Show me every deal we lost to [Competitor X] where pricing was the stated objection” becomes a 10-second query, not a 10-hour research project. It’s like having a Chief of Staff in your Slack 24/7.

12. Monitor sales methodology compliance at scale.

Every call gets evaluated against MEDDIC/BANT/SPICED criteria to determine whether reps validated each element vs. just mentioned a keyword. AI coaching scorecards can auto-grade every call against your chosen framework. No more moderating debates on whether a deal is an SQL. Create the rubric, then AI applies it semantically at scale.

The distinction between “mentioned” and “validated” is critical here. A rep saying “so who’s the decision maker?” is not the same as confirming that the Economic Buyer has budget authority, a timeline, and will attend the next call. AI can tell the difference. One sales team used this to move MEDDICC-specific language from 2% to 17% of call time, and more importantly, saw their win rates on qualified pipeline jump proportionally. Without AI, managers review maybe 3–5 calls per rep per month. With it, every call gets scored.

13. Build closed-lost re-engagement campaigns from objection mining.

This is one of the highest-ROI plays on this list because the pipeline already exists. You’ve already done the discovery. You’ve already built the relationship. The deal just didn’t close for a specific, documented reason.

The specific objection from every lost deal (pricing, feature gap, timing, competitor) gets extracted and segmented. RevOps builds targeted sequences per cluster: “pricing changed” or “we built that feature” or “competitor renewal approaching.”

When that reason changes (you ship the feature, you adjust pricing, their competitor contract comes up for renewal), you have a warm re-engagement opportunity with full context. Now you can have an evergreen play that uses your own first-party data instead of starting from scratch.

14. Refine ICP from won-deal conversation patterns.

What do your closed-won transcripts have in common? Not the firmographic data (industry, headcount, revenue) that lives in your CRM, but the conversational patterns. Which problems do they mention? What buying triggers come up? What language do they use?

This goes deeper than standard ICP analysis. You might discover that prospects who mention “board pressure to reduce vendor count” close 40% faster than the rest. Or that deals where “security review” comes up in the first call (not the third) have 2x the win rate. These are signals that live in transcripts, not in your enrichment data. Mining them lets you build an ICP based on how your best customers actually talk about their problems, which is far more useful for targeting and messaging than industry + headcount alone.

15. Analyze call patterns to compress sales cycles.

Which call sequences, cadences, and topics correlate with the shortest won deals? The data reveals the pattern, and it’s often not what you’d expect.

Maybe your fastest deals always have a technical deep-dive in call two (not call four). Maybe deals where the AE sends a follow-up within 30 minutes close 15 days faster on average. Maybe three calls in the first 10 days beats five calls spread across six weeks. Top performers manage 2.6x more deals with sales cycles 42% shorter. When you analyze the transcript data, you often find it’s because of a process (a specific sequence of topics covered in a specific order). Once you find the pattern, you can coach every rep to run it.

35 More Quick Hits

Those 15 plays are a great place to start. And here are another 35, that should get your creative juices flowing, grouped by function.

Sales

16. Build SDR-to-AE handoff packages from call clips. SDRs clip the exact moments where the prospect stated their problem, budget range, and requirements, so the AE walks in with full context instead of a one-paragraph CRM note.

17. Route cross-sell opportunities from account conversations. When a customer mentions a challenge solvable by a different product line, the system flags it and auto-notifies the right team with context. (Attention.com has a dedicated Cross-Sell Radar agent for this.)

18. Detect buying signals to optimize pitch timing. Transcript analysis reveals which questions or moments consistently trigger pricing discussions, then coaches reps on when to introduce value vs. when to hold.

19. Generate mutual action plans from call language. Milestones, deadlines, and stakeholder responsibilities get extracted from transcripts and dropped into a shared MAP.

20. Run pricing negotiation prep from historical patterns. Before a negotiation call, AI pulls the most common objections from similar personas, the highest-converting rebuttals from top performers, and discount thresholds from comparable deals.

21. Optimize executive sponsor engagement timing. Transcript data reveals when VP+ contacts actually engage across the deal cycle.

22. Customize proposals from transcript insights. Discovery calls contain what the buyer said matters most. The system pulls those priorities and populates proposal templates that mirror them.

23. A/B test cold call opening lines from transcript data. Tag which opener each SDR used and correlate with outcomes across hundreds of calls. Fun fact: “How have you been?” delivers a 6.6x higher meeting-booking rate vs. “Did I catch you at a bad time?”

24. Bridge discovery to demo with AI-generated prep briefs. The buyer’s top use cases and competitive concerns get extracted from discovery and mapped to specific product features, so the AE leads with what matters.

RevOps

25. Validate stage gates from transcript evidence. The system cross-checks whether conversation evidence supports the stage a rep advanced to. Did the Economic Buyer actually participate? Was budget confirmed? Deals that fail get flagged with a specific gap report.

26. Automate CRM hygiene from transcript cross-referencing. Next steps, objections, competitor mentions, and deal signals get extracted from calls and written to CRM fields, then discrepancies between what the rep entered and what was discussed get flagged.

27. Score cross-team handoff quality. What the SDR discussed gets compared with what the AE covered. Did the prospect have to repeat themselves? Each handoff gets a quality score and RevOps identifies systemic failures.

28. Run pricing and discount pattern analytics. Negotiation transcripts get scanned to identify which reps discount too early, which concession patterns correlate with better margins, and the revenue impact of unnecessary discounting.

29. Mine competitive intelligence from the full call library. Every competitor mention gets aggregated with surrounding context, and positioning shifts get tracked quarter over quarter. Attention.com’s Competitive Intelligence Tracker delivers weekly reports out of the box.

30. Build and maintain curated call libraries for onboarding. RevOps sets filters (closed-won, above-average ACV, top performers) and the system auto-updates every 30 days, so the library never goes stale.

31. Rebalance territories using conversation effort data. Instead of account count alone, RevOps layers in call duration, sentiment, multi-threading depth, and deal complexity. Better territory design can increase revenue 2–7% without changing strategy.

32. Identify process bottlenecks from call topic clustering. If 60% of deals stalling at “Technical Validation” involve calls where prospects ask about security compliance but no SE is present, that’s a process bottleneck you can fix.

Customer Success

33. Build a churn early warning system from call sentiment. Every CS call gets an automated sentiment score, and calls below a threshold auto-post to a “Churn Risk” Slack channel.

34. Detect expansion signals from customer conversations. Growth plans, new team hires, feature requests aligned with premium tiers, and positive adoption language all get flagged and surfaced while momentum is high.

35. Eliminate context loss on sales-to-CS handoffs. Every promise sales made, every success criteria discussed, and every stakeholder’s priorities get compiled across all deal calls so the CS team starts onboarding with full context.

36. Auto-generate QBR prep from quarterly call synthesis. All recorded interactions for a specific account over the past quarter get compiled into a structured executive summary. Teams report cutting QBR prep from 8–12 hours to under 60 minutes.

37. Score customer health from conversation signals beyond usage. Calls, emails, and support interactions get evaluated for sentiment direction, escalation language, and renewal timing mentions, catching accounts with high usage but declining call sentiment.

Marketing & Product

38. Mine voice-of-customer for messaging and ad copy. Hundreds of call transcripts reveal how prospects actually describe their problems. One company feeds extracted keywords directly into Google Ads, so their campaigns use the words customers actually use.

39. Aggregate competitive intelligence from live deal conversations. Marketing builds living battlecards with real buyer language and tracks competitive positioning shifts over time.

40. Generate content at scale from anonymized transcripts. PII gets redacted, then anonymized call conversations get rewritten into SEO-optimized blog posts.

41. Track campaign attribution through conversation mentions. When prospects mention your podcast, benchmark report, or billboard in a sales call, it gets captured, closing the loop between brand investment and deal creation in ways UTMs never could.

42. Build an objection-driven content strategy. (This is a personal fave.) Objections get ranked by frequency and correlated with deal outcomes. Marketing builds content that proactively addresses the most common deal-killers before they arise in the sales cycle.

43. Aggregate feature requests routed directly to the product board. “I wish” phrases and feature names get tracked across all calls, ranked by frequency, tagged with customer segment and ARR, and product managers get linked to the exact call moments.

44. Run competitive feature gap analysis from lost-deal transcripts. Which competitor features were cited as differentiators in lost deals? The transcripts turn anecdotal “we lost on feature Y” into quantified gap data for roadmap decisions.

45. Detect bugs and product issues from support call patterns. When mention frequency crosses a threshold, the product team gets auto-notified with aggregated context on how many customers mentioned it, with clips of their frustration.

Enablement/Management

46. Structure new hire onboarding around real deal arcs. New reps follow a full deal from the first discovery call through close, hearing how top performers navigated each stage. Teams using this method had 50% faster ramp time and 64% more revenue in the first quarter.

47. Generate AI coaching scorecards with automated rep feedback. After every call, discovery quality, objection handling, methodology adherence, and talk-to-listen ratio get scored with timestamped examples. Attention.com’s coaching scorecards auto-select the right rubric per call type.

48. Build a discovery question repository from top-performer calls. Enablement extracts every question top-converting reps ask, organized by deal stage and persona, then validates whether other reps are asking the right qualification questions.

49. Detect behavior drift with real-time leadership alerts. Reps offering discounts too early? Skipping discovery? When calls match these patterns, alerts send to managers via Slack.

50. Generate AI roleplay scenarios from real customer conversations. Enablement feeds real call scenarios into AI training tools that create interactive roleplays with scoring. One team built 14 training courses with 75+ scenarios in three hours.

You’re sitting on a treasure trove of data in your customer interactions. All you have to do is activate it with a bit of creativity. I hope these plays spark some ideas and give you some quick wins.

The teams running a bunch of these plays simultaneously will be operating at a fundamentally different level than everyone else. The data is already sitting there. These plays just put it to work.

Platforms like Attention.com are accelerating this by moving past passive analysis toward AI agents that act on your data. So if you’re looking to get AI agents deployed across RevOps (and more), reach out to the team at Attention.com and let them know The Signal sent you.

Thank you for your continued attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

PS: Don’t forget to secure your spot for our live session on this topic on Thursday, May 7th.

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11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

I recently had a chance to sit down with Alec Paul, the founder of SalesBrand.

He’s the guy behind some of the best B2B CEO brands on LinkedIn (like Adam Robinson, Gal Aga, and others). He doesn’t post for them (he’s not a “ghostwriter”). He teaches them how to post for themselves, what to say, and how to say it. Hence: “The Rick Rubin of LinkedIn” (as Sam Jacobs described him). He makes the artist sound like themselves, but better.

I asked Alec to look over my LinkedIn feed and rip it apart. And, apart, he ripped it.

I recorded the conversation (just for myself). But then, after the fact, I realized some other people would find it valuable. So I asked Alec if I could share a summary of our conversation, and he said yes. So, here we are. :)

So, let’s get to it.

Tip #1: ➜ “Double my impressions” is not a silly goal

At the beginning of the call, Alec asked me what my goal was, and I (reluctantly) told him: I want to double my impressions on LinkedIn. I half-expected him to tell me that was a vanity metric.

Instead, he said, “That’s not silly. That’s a great goal.”

But with one important qualifier: it needs to be the right impressions. Doubling your reach means nothing if you’re reaching the wrong people. If your content starts going wide and attracting a general audience that doesn’t match your niche, you’ve traded depth for breadth. And for a newsletter like The Signal, depth is the whole business model. The readers are coming back because the content is made for them, not for everyone.

So yes, chase eyeballs. But measure the quality of the views, not just how many.

Tip #2: Spread your posts out

I was posting three times in one day, then going dark for three days. Alec’s reaction was immediate: stop doing that.

Spread them out. One thought leadership post. One lighter post (more on that later). Consistency matters more than volume spikes. The algorithm rewards it and your audience starts expecting you.

His rule of thumb when you do post more than once in a day: space them at least 4 hours apart. Back-to-back posts cannibalize each other’s reach.

Tip #3: Every post needs to pass the “What is this about?” test in two lines

This one was the biggest takeaway from my conversation with Alec.

Alec scrolled through my feed and kept saying the same thing: “I have no idea what this post is about.” He’d get to line four or five before figuring out the topic. “By then, most people have already scrolled past.”

If a reader can’t tell what your post is about in the first two lines, you’ve already lost them. The hook can’t be an afterthought. It’s the thing that gets people to stop scrolling on their feed.

Related: Alec’s 10% Strong-Opinion Test. If less than 10% of people strongly disagree with you, your opinion isn’t strong enough. The reaction you’re chasing is “Amen, someone finally said that” from part of the audience while others push back.

Tip #4: Screenshot, don’t “reshare”

Alec saw what I’ve been doing and said he’s seen it work for others too. Where you screenshot a tweet, paste it into a LinkedIn post, add your own commentary on top. It consistently outperforms LinkedIn’s native reshare feature (I wish LinkedIn made an effort to make “reshares” a real part of their feed, like it is on X… maybe one day… cc LinkedIns-Product-Team).

The screenshot is the visual hook. Your commentary is the post.

Tip #5: Never double-link

I had a post with both a Substack link and a Luma link. Alec flagged it instantly. “I don’t think I would do the double link.”

One link per post. Pick the one that matters most. If you’re torn, put the secondary link in the first comment.

Tip #6: Cut the link preview

LinkedIn auto-generates a link preview box when you include a URL. It looks terrible and it tanks reach. Alec pointed to multiple posts of mine where the link preview was the problem (see example below).

The fix: use an image instead of letting LinkedIn auto-generate the preview. Put the link in the first comment, or write it as plain text (eg: TheSignal.club; no https://).

Note: If you schedule a post and then edit it after publishing, the link preview often comes back. Watch for that.

Tip #7: Tag strategically, or don’t tag at all

I had a post with a bunch of company tags. Alec’s framework: only tag companies and people you believe will actually engage. Clay and Attention will engage. Salesforce and HubSpot probably won’t.

Too many tags, especially from big companies that aren’t going to respond, makes it look like engagement bait. Small, LinkedIn-active companies and individual operators are worth tagging. The big dogs are busy and don’t care. And that hurts reach.

Tip #8: Link to conversion-optimized pages

I don’t know how to solve this yet (since I use Substack).

Alec looked at my Substack page and said it’s not designed to convert. He’s right. Substack is great for writing and distribution, but the landing pages aren’t optimized for cold traffic.

His recommendation: for gated content (reports, data, tools), link to a dedicated page with an email gate. Not Substack. I may test this in the future. The newsletter link in my LinkedIn bio and profile can still point to TheSignal.club, but when I’m driving traffic from a specific post, it should go to a page that’s built to capture emails.

Tip #9: Collect outcome-based social proof, not compliments

People DM me all the time saying they love my content, and sometimes I share those. Alec’s point: “that’s nice, but it’s not useful for growing.”

The social proof that actually converts is outcome-based. “I attended your MCP webinar last quarter, built an automated account scoring system the next week, and got promoted two months later.” That social proof is a great LinkedIn post. “Your content is amazing” is not.

When someone messages you saying they love what you’re doing/building, dig deeper. Ask what they achieved because of what you shared. Ask what changed. Get the outcome. Then turn it that into a post (with their permission).

Tip #10: Break your newsletters into individual LinkedIn posts

I published a piece called “9 Lessons from 11 Growth-Stage Companies Building GTM Agents In-House.” It did well on Substack (above my average number of views for a piece). Alec looked at it and immediately asked: “Did you break all these 9 lessons into individual posts?”

I hadn’t. I should have. (I still should.)

Each lesson is its own LinkedIn post. “Here’s how Vercel built an inbound AI agent. Here’s the stack they used.” That’s a week of content from one newsletter. I sometimes spend 15+ hours on a Substack piece. But don’t spend any time driving traffic to it (outside of existing subscribers). This is something I need to spend more time doing.

Tip #11: Nichemaxxing

Alec shared an article with me, where TBPN’s Isaac Medeiros coined a term: nichemaxxing. The idea is that if The Signal ever hits 100,000 subscribers, I’ve failed.

That sounds counterintuitive. But it means I’ve gone too broad. I’ve diluted the audience. I’ve started writing for “people interested in business” instead of “B2B GTM operators who want to know how the best teams are using AI right now.”

As Isaac put it:

How do brands win in a world where content is democratized, algorithms feed you perfectly tailored interests, and everyone can compete for attention? The answer is nichemaxxing. Real experts. Real points of view. Real people delivering real value. Things AI can’t easily reproduce.

In a world where AI can generate infinite generic content, the moat is specificity. A niche audience, served by someone with real experience in their exact world, is the one thing that gets harder to replicate, not easier.

This works for a SaaS founder building content as they try to win a market.

Tip #12: “Has changed,” not “is changing”

Small language detail, big positioning implication. I was describing The Signal’s thesis as “the go-to-market playbook is changing.” Alec corrected me in real time: “Has changed.”

He’s right. “Is changing” is passive. It implies you’re watching from the sidelines. “Has changed” is definitive. It implies you know what the new playbook is and you’re going to show them.

The meta point here is: every word in your positioning matters. This one word made the whole thing sharper. You have to take a stance to stand out. Controversial takes get attention.

Tip #13: Video matters, but start scrappy

Alec and I talked about video for a while. His take: Zoom-style talking head video is going to be hard on LinkedIn because the feed is saturated with it. Everyone’s on Zoom all day. They don’t want more of it in their content.

If I’m going to do video, it needs to be either high-production (not necessarily expensive, but better than a Zoom call) or interview-based with screen shares showing real workflows and tools. The visual has to be interesting enough to stop the scroll.

But he also said, “Don’t wait for the perfect setup.” TBPN’s first episode was filmed in a terrible-looking office. Start now, improve over time.

Tip #14: Don’t invest in the company page

I asked Alec if I should be posting more on The Signal’s LinkedIn company page. His answer was fast: “Not worth your effort, probably. You can keep it alive so it looks like you have a presence. But the reach from a company page is a fraction of what you’ll get from your personal profile. And the time you spend trying to grow it is time you’re not spending making your personal content better.”

Anecdotally, I almost never read posts from company pages (I feel like they are ads), but I read plenty of posts from founders talking about their products/markets. People buy from people. Same thing applies here.

A few more from Alec’s playbook

These didn’t come up in our conversation, but they’re worth stealing. I found these when I (Claude) was doing research on Alec’s perspectives:

15. Old Way vs. New Way. Don’t post about your company and how great it is. Contrast how something used to be done with how it should be done now, and cast yourself as the narrator of the shift. Your company becomes a character in your story, not the subject of it. (source)

16. Content Market Fit. Post 10–15 times, then look at which posts drove profile views and DMs, not likes. Double down on those formats. That’s how you find your version of PMF for content. (source)

17. Lead your headline with your title, and use “CEO” not “Founder.” At VP and above, leading with your title means other execs accept connection requests without clicking through. “CEO” also signals more authority than “Founder” on LinkedIn. (source)

18. The first-comment cheat code. Follow 2–3 creators with 100K+ followers, ring their bell, and jump on the post immediately with a substantive comment. An alternative framework, a resource, a real opinion. Not “great post.” Your comment shows up in their audience’s feed, passively. (source)

19. The “steal their audience” search hack. LinkedIn Search → People → All filters → set location + industry → Followers of → add a competitor or industry authority → Show results. Instant ICP list, no Sales Nav required. (source)

20. Don’t hire a ghostwriter. This is Alec’s own position, despite being one of the best in the game: “The main thought leaders write their content. Once you start, it gets easier.” He calls his role “publicist and acting coach,” not writer. (source)

21. AI-written posts have a Welshian accent. His warning on AI tools: “ChatGPT was trained on Justin Welsh, and that’s why 90% of the AI LinkedIn posts you see have a Welshian flair to them.” The tools make everyone sound the same, which undercuts the one thing that actually works: authenticity. Avoid AI-slop! (source)

I believe founders and operators who build authentic audiences on LinkedIn over the next 12 months will have a meaningful distribution advantage over everyone else. The ones who wait will be competing in a much more crowded field.

And this will be even more true, as our feeds fill up with more AI slop with each passing day.

Follow/reach out to Alec Paul to learn more from the guru, directly. He’s legit.

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Related posts:

• Attention is the Scarce Resource in the AI Era Why More Founders Should Leverage LinkedIn Content in their GTM Strategy

• The Increasing Value of Quality Content as part of the Modern Marketing Playbook

• The Barbell Strategy of Pipe Gen

Subscribe now

Thanks for reading this hand-rolled, 100% organic, farm-to-table (human-written) newsletter.

Presented by Clay.

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps

Clarify → The Autonomous CRM

Nooks → Agent Workspace for Intelligent Outbound

Sumble → Buying signals from tech stacks, people data, & active projects

11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

Another dizzying week in AI-land. Anthropic launched Claude Design (60M videos on their launch video on X is bonkers), and allegedly killed Figma overnight (its stock was down 8% on the news). And SpaceX announced a deal with Cursor, for the right to acquire it for $60B later this year, or to pay $10B for work they’re doing together. The companies said they’ll collaborate to build “the world’s best coding and knowledge work AI.” (Oh, and SBF would have been up 15,000x on his investment in Cursor, totaling $114B had the FTX holdings not been sold in a fire sale.)

I can’t keep up. But, keep up, we must. Speaking of…

Every company knows they should be using AI in their go-to-market org. The fastest-growing companies have at least one person dedicated to this role.

Yesterday, while eating my lunch, I was listening to the latest 20VC podcast, and when Aaron Levie went on this rant (clip below), I was screaming “that’s a GTM Engineer!” (or AI Ops. Or <whatever you want to call it>).

The point is, the smartest companies have someone helping their GTM team build leverage through systems, automation, and AI. Instead of just scaling with headcount.

Listen to the 40-second clip below:

It’s different than traditional JTBD of a RevOps person (though this person could sit under the RevOps org).

So, naturally, I went down a rabbit hole that led me to our second Research Report by The Signal.

Here’s the punchline:

→ 54% of the fastest-growing B2B SaaS companies have ≥1 GTM Engineer.

Here’s what’s in today’s Research Report:

• The surface-level count is a trap

• The hidden bench of (GTME) talent

• If you’re hiring one

• Top technologies in GTME job postings

• The companies without a GTM Engineer

• What I think happens next

Alright, let’s get into it.

The surface-level count is a trap

The title “GTM Engineer” is the newest, shiniest label, but the function has existed under different names at Stripe, Ramp, Databricks, and others for a while. Clay didn’t invent the job. But they did standardize the name.

I pulled the numbers across 63 of the fastest-growing SaaS companies I track (OpenAI, Anthropic, Stripe, Ramp, Notion, Databricks, Cursor, Vercel, Webflow, Plaid, and 53 others). When I filtered on the literal “GTM Engineer” title, only 10 companies showed up. That’s the story most people believe: “GTM Engineer” is a Clay phenomenon, everyone else has one token hire at most.

That story is wrong. Once I looked at the actual work instead of the label, 34 of 63 companies have someone doing GTM Engineering today.

When I ran the strict title search first, here’s what came back: Clay (20), Vercel (2), Webflow (2), 1Password, Abnormal, Anthropic, Canva, Databricks, Descript, Miro (1 each).

If I stopped there, the story would be “Clay invented a role nobody else has really adopted.” That’s what most people say when they dismiss GTM Engineers as a fad. It’s also why most hiring managers still think of this as an experiment rather than a new role that is necessary to scale an AI-native GTM org.

But the word “engineer” is doing a lot of work in that title. The companies that have been doing this for years didn’t use the term “GTM Engineer.” They’d often be called “Growth Engineers.”

The hidden bench of (GTME) talent

When I dropped the strict title match and looked at any combination of “GTM” plus “engineer” or “engineering” (regardless of phrasing), the picture changed completely. Shout-out to Sumble, inside Claude Code, for helping me wrangle this data.

A few examples of adjacent titles found:

• GTM Systems Engineer, Senior Engineer / Member of GTM Team, GTM Field Readiness Engineer, Senior GTM Systems Engineer, Data Engineering Manager – GTM, Data Science Manager – GTM, Lead Analytics Engineer for Marketing and Sales, Analytics Engineer – GTM, and GTM Systems Engineer.

If we add in Growth Engineers (Stripe, Supabase, Grafana Labs, HeyGen, Figma, Monte Carlo, Postman, Scribe) and the Hightouch/Fireworks-style “GTM & Growth | Data and AI” ICs where the word “engineer” doesn’t appear in the title, but the work obviously is, it adds another 14 companies (we’ll call these “Tier 2”), and brings the total to 34 companies.

If you’re hiring one

Stop screening by a specific title. The best candidates for a GTM Engineer role right now are often titled Analytics Engineer, Growth Engineer, GTM Systems Engineer, or Data Engineer on a revenue-adjacent team. They maybe haven’t ever applied to a “GTM Engineer” role because the title didn’t exist when they took their current one.

The skills that matter: SQL, API integrations, comfort with tools like Clay and Claude Code, ability to read a GTM motion end-to-end.

If you’re looking for a framework on when to hire a GTM Engineer, I wrote about that last April. The short version: if you have a manual playbook that’s working today, a GTM Engineer can help you scale it. That hasn’t changed. What’s changed is that you have a much bigger candidate pool than “people with the exact title.”

Top technologies in GTME job postings

Here are the most frequently mentioned tools and technologies in GTME job listings:

Using Clay does not make you a GTM Engineer. But, if you’re a GTM Engineer, you likely know how to use Clay. :) And you’ve probably gone down the Claude Code rabbit hole the last few months. Avoid “Gear Acquisition Syndrome.” But at the same time, use the latest tools to give you leverage.

The companies without a GTM Engineer

29 of the 63 fastest-growing private B2B SaaS companies still don’t have a GTM Engineer.

Some are genuinely early-stage (Decagon, Abridge). Some are running lean PLG motions without many sellers.

And others have the role dispersed in other places. For example, I asked Kyle Norton, CRO of Owner, if I was missing something in the research I did, and he told me: “We have people doing the GTME jobs to be done but we don’t call it that. It’s Applied AI and Data Science doing it.”

What I think happens next

Again, 54% of the fastest-growing private B2B SaaS companies have a GTM Engineer.

That’s a pretty wild stat, considering only between 3-7% of private B2B SaaS companies have at least one GTM Engineer on staff (with the lower bound applying when you insist on the literal job title and the upper bound when you include adjacent Growth Engineer and GTM-scoped RevOps Engineer roles). This collapses to <1% at seed/Series A and rises to 8%+ at scale-up and enterprise SaaS. [Source]

This means the fastest-growing B2B SaaS companies are an order of magnitude more likely to have at least one GTM Engineer in-house.

In 12 months, it’s likely the industry will converge on a single title. Maybe “GTM Engineer.” Or GTM Architect, Growth Eng, or something else.

But semantics (on the title) aside, I predict this chart continues to grow over time.

GTM Engineer job postings by year (2022–2026):

*Note: 2026 projection is 826 postings through day 112 of 365, linearly extrapolated to a full-year run-rate of ~2,692. Actual full-year total will vary with hiring seasonality.

The category went from nonexistent to one of the fastest-growing GTM roles in three years.

Every company knows they should be using AI in their go-to-market org to scale without adding too much headcount.

54% of the fastest-growing companies have at least one GTM Engineer—someone dedicated to helping their GTM team build leverage through systems, automation, and AI.

Let’s see what this data looks like in a year. (Or who knows, maybe by then ASI is here, and none of this will matter anyway.)

As always, thank you for your attention and trust. I do not take it for granted.

PS

↳ ICYMI, our first Research Report was on: The GTM Tech Stacks of the Fastest-Growing Private B2B Companies. Find the most up-to-date data here: ProspectStack.com/top-tech-stacks. I hope it’s helpful!

See you next time,
Brendan 🫡

Subscribe now

Thanks for reading this hand-rolled, 100% organic, farm-to-table (human-written) newsletter.

Presented by Clay.

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps

Clarify → The Autonomous CRM

Nooks → Agent Workspace for Intelligent Outbound

Sumble → Buying signals from tech stacks, people data, & active projects

11x.ai → Accelerate growth with a digital GTM workforce

Happy Friday, y’all! 👋

Things continue to ramp up (including Allbirds—yes, the shoe company—pivoting to an AI company and ripping 600% in a day lol). Plus, potentially the biggest day in AI so far this year: Claude, Codex and Perplexity all with massive drops.

Today’s post is a little shorter than usual, and a seed of an idea that I hope to flesh out more over the coming months. Reach out to let me know how you’re building your GTM motion to support AI agents as a new buyer.

ICYMI: Recap of our live event on Tuesday (including ~7 min videos of each of the 6 presentations) → 6 Ways GTM Teams are using MCPs and Claude/Claude Code Today

Six months ago, if you asked me what an AI-native GTM playbook looks like, I would have said: the same motions as 2022, but supercharged with AI. Better outbound. Smarter enrichment. AI workflows for ICP mapping and tiering. A GTM Engineer running workflows instead of an SDR manually copy-pasting from a spreadsheet. Same motions, faster/better execution.

I don’t think that’s the full picture anymore.

The more I build inside Claude, the more I notice something that should worry (or excite) every GTM leader reading this: I’m not buying the way I used to. I’m buying through agents. And I think a lot of your future customers will be, too.

Here’s what we’re covering today:

• What “AI agents as buyers” actually looks like in practice

• Why the GTM motions themselves change (not just the tools)

• Meet your new buyer persona: “AI Agent Amy”

• What to build for right now

• The asymmetric bet

Let’s get into it.

What “AI agents as buyers” looks like in practice

I use Supabase as my database. I’ve never been to their website. I’ve never requested a demo. I’ve never signed up for a free trial through their marketing site. I discovered it, evaluated it, and started using it entirely through Claude.

Same story with Resend. And a growing list of tools.

And the data backs this up. Lavanya Shukla (formerly Head of Growth at Weights & Biases, now Managing Partner at Improbability.vc) published a piece called The Agent Flywheel that put numbers to what I’ve been feeling anecdotally.

When Claude Code adds email to a project, it picks Resend 63% of the time. It picks SendGrid 7% of the time. SendGrid has been around since 2009. Twilio paid $3B for it. They have a full enterprise sales motion, dedicated IPs, and 15 years of track record. None of that matters to an agent.

Resend launched in 2023 with zero users and now has 500K developers. Supabase went from 1M to 4.5M developers in 12 months. As Lavanya put it, “your product is no longer your UI. It’s your API.”

The companies I keep reaching for are the ones that are most integrated into the agentic workflow I’m already in. They have a robust MCP integration. They have a CLI that actually works. Their API is clean. Their docs are written for machines, not just humans.

I didn’t go looking for these companies. They were surfaced by the tools and workflows I was already building in. That’s a fundamentally different buying motion than anything we’ve seen in B2B SaaS.

Why the motions themselves change

If your buyer is a person, you send cold emails. You run events. You build inbound funnels. You create content for humans to read, evaluate, and share internally.

If your buyer is an agent, none of those motions work the same way. You’re not going to send a cold email to an AI agent. But you are going to optimize for AEO (agent engine optimization, the next evolution of SEO). You are going to build support documentation that looks very different for an agent versus a person. You are going to build features and tooling designed for agent-first workflows rather than human-first workflows.

I should note: I don’t think AI Agent Buyers will fully replace Human Buyers—instead, it will be an additional buyer that GTM teams/workflows will need to support.

Here’s a visual we put together to show this “old world” vs. “new world” evolution:

Meet your new buyer persona: “AI Agent Amy”

Every GTM team has the slide. Marketing Mary. CIO Steve. Personas with names and headshots and buying preferences.

I think every company now needs to add a new persona to that deck. I’ll call her “AI Agent Amy.”

AI Agent Amy doesn’t read your blog posts. She doesn’t attend your webinars. She doesn’t click your nurture emails. But she does evaluate your API documentation. She does check whether you have an MCP server. She does look at your developer community to see if real people are building real workflows on your platform.

Right now, AI Agent Amy might account for 1% of your pipeline influence. Maybe less. The bet is whether that 1% becomes 10% in 12 months. Or 30%. Or more than 50%.

If you believe that agentic workflows are here to stay and growing (I do), then building for this persona isn’t optional. It’s an asymmetric bet with huge upside and very little downside.

What to build for right now

This is early. There is no playbook yet. But the pattern I keep seeing from the companies that are winning in agent-first workflows comes down to a few things:

A world-class API and CLI. Not an afterthought, not a v1 that barely works. The real thing, battle-tested.

Native MCP integrations. If your product doesn’t show up inside Claude, Cursor, or the agentic coding tools your buyers are living in, you don’t exist to this persona. Even Uncle Benioff is going this route (honestly, impressive to see). “Our API is the UI.”

Documentation written for agents, not just humans. This means structured, predictable, complete. Agents don’t skim. They parse. And here’s the uncomfortable truth from Lavanya’s analysis: agents will choose a slightly worse product if it’s easier to implement. From the agent’s perspective, a product it can execute is strictly better than one it can’t. Technical superiority means nothing if an agent can’t figure out how to use you.

Community-driven use cases. The companies winning here have active communities showing how to build real workflows on top of their tools. That’s the new word-of-mouth for AI Agent Amy. And it creates a flywheel: developers who succeed with your product publish their configs, other developers clone them, and your tool spreads across thousands of agent deployments at once. Each cycle makes the next one easier.

The asymmetric bet

I want to be clear: I am not saying AI is going to replace salespeople. I am saying AI is going to replace a growing percentage of buyers. The human on the other side of the deal is still there. But the discovery, evaluation, and initial adoption? That’s increasingly happening through agents and agentic workflows, not through your website’s pricing page.

If you believe that trend compounds over time (like I do), then right now is the time to start building for it. Not that you need to overhaul your entire GTM motion tomorrow, but the companies that start now will have a meaningful head start on everyone else in 12 months.

Andrej Karpathy said it best:

“It’s 2026. Build. For. Agents.”

If you’re building new, creative GTM approaches specifically geared toward AI agents as a buyer persona, I want to hear about it. Reach out. I’d love to showcase what you’re building in the coming months.

As always, thank you for your attention and trust. I do not take it for granted.

Other nuggets from the week:

Kyle does it again, arguably the most AI-native CRO on planet earth: What World-Class AI in GTM Looks Like

Bret Taylor of Sierra on AI agents, outcome-based pricing, and the OpenAI board - on Cheeky Pint (with John Collison, co-founder of Stripe):

Have a great weekend!

See you next week,
Brendan 🫡

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Presented by Clay.

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps

Clarify → The Autonomous CRM

Nooks → Agent Workspace for Intelligent Outbound

Sumble → Buying signals from tech stacks, people data, & active projects

11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

A couple of weeks ago, I put out a post on LinkedIn that basically said: I want to gather some GTM operators to share examples of AI agents/workflows they’ve built using MCPs and Claude/Claude Code. There was a lot of interest, so I put together a live event. Close to 800 people registered. And almost 400 builders joined live (wylin). Six people presented what they’ve built using MCPs inside Claude (across chat, cowork, and code).

And what I saw across all six presentations was a pattern I think most GTM teams are going to feel very soon: the gap between teams that have connected their tools to AI and teams that haven’t is getting wider every week.

MCPs are the connective tissue. They let Claude talk to your CRM, your data warehouse, your call recorder, your prospecting tools. Without them, Claude is smart but blind. With them, it becomes something closer to a second brain that actually knows your business.

• Claude is powerful.

• GTM tools are powerful.

• But when you combine the two, it’s a real “1+1=10” situation.

I had a blast seeing all the creative ways GTM teams are using MCPs to build inside of Claude. It’s a pleasure to learn alongside everyone at this particular moment in time.

I hope this post inspires you to build something for your team. Even if that means you don’t read this entire article. That’d be a win in my book. :)

Each presentation has a video on YouTube (smash that subscribe button on The Signal’s YouTube channel while you’re there 😂).

Here are the use cases:

1. How an SMB AE at Zendesk Uses Sumble + Claude Cowork to Build Micro-Campaigns (Eric Fitz, AE at Zendesk)

2. How Nooks’ RevOps Team Created a Company Brain with Claude Code by Building a DIY Data Warehouse and a custom MCP to query data from HubSpot, Mixpanel, Nooks, and more (Josh Nelson, RevOps/GTM Engineer at Nooks)

3. How Clarify’s MCP Turns Claude into a Full-Cycle Sales Assistant (Vinayak Mehta, Software Engineer at Clarify)

4. How Attention Auto-Clusters Churning Prospects and Triggers Targeted Outreach with Claude Code (Anis Bennaceur, Co-founder & CEO at Attention)

5. How WorkSpan Built a Headless Context Layer so Reps Get Everything They Need Directly in Claude Chat (Sam Gong, SVP Marketing at Workspan)

6. How 25 Agents in Slack Run MintMCP’s GTM Org, Built with Claude Code and Custom MCPs (Jiquan Ngiam, Co-founder & CEO at MintMCP)

Alright, let’s get into it.

1. How an SMB AE at Zendesk Uses Sumble + Claude Cowork to Build Micro-Campaigns

(Eric Fitz, AE at Zendesk)

Eric is an SMB AE at Zendesk. He has 1,800 accounts. Every rep who’s had his territory for the past five years has had the same data. He needed an edge and found it in Sumble.

Eric’s workflow starts in Claude Cowork with the Sumble MCP connected. He uses Cowork (not chat) because it needs a bigger context window. He uploads a CSV of his full account list, then asks Claude to do something very specific: identify which of his 1,800 accounts have call center technology.

Why call centers? Because at Zendesk, if a company runs a call center, they auto-qualify for every other type of support Zendesk offers. It’s the biggest deal type. And until Sumble, there was no tool that could reliably surface call center usage.

Sumble pulls this from a combination of HTML stack detection, job posting data, and historical hiring signals. Out of Eric’s 1,800 accounts, it found 61 companies with call center tags.

From there, Claude finds director-level+ contacts at those companies using Sumble’s MCP, then drafts personalized emails for each one. Eric showed a draft where Claude referenced the prospect’s exact tech stack (TalkDesk, Five9, Genesis) in the subject line. Nobody else is sending that subject line.

Eric also built what he calls the “Zendesk Sales Brain,” a Claude Skill where he pointed Claude at Zendesk’s public-facing Help Center and had it learn every major sales play, competitive positioning angle, and product capability. That context now feeds every prompt.

Eric works in batches of ~30 contacts per micro-campaign. At a 4% reply rate, that’s one reply per batch. If he runs 30 batches, that’s 900 contacts and 30 potential conversations. He drafts them in Claude Cowork, which pushes them into Gmail drafts, and then he sends them from his phone.

Eric said he’s sent roughly 5,000 emails using Sumble-sourced data, and only 10 have bounced. That’s a 99.8% deliverability rate.

He showed an example where the workflow surfaced a contact whose LinkedIn title was “Implementation Manager” but whose actual job description (pulled by Sumble) showed she runs the entire call center. Eric explained how he never would have found her searching by title alone. The MCP is what makes Sumble’s data layer actionable inside Claude rather than living in a separate tab.

• Try Sumble (it’s free) at Sumble.com. Then set up their MCP inside Claude. Have fun. :)

• Connect with Eric on LinkedIn.

2. How Nooks’ RevOps Team Created a Company Brain with Claude Code by Building a DIY Data Warehouse and a custom MCP to query data from HubSpot, Mixpanel, Nooks, and mor

(Josh Nelson, RevOps/GTM Engineer at Nooks)

Josh is a GTM Systems Engineer at Nooks. Small RevOps team. And like most small ops teams, he was drowning in ad hoc data requests. Finance needs a list of CSMs for every invoice. Leadership wants to know how many calls an SDR made that turned into closed-won deals. Everybody wants a report, and the data lives in five different systems. Classic.

So, naturally, Josh decided to build a data warehouse from scratch 🤪. During a hackathon, using Claude Code.

He set up a Postgres database on Render (free tier to start, a couple hundred bucks a month once the data volume grew), then built ETL pipelines that pull data from HubSpot, Mixpanel, Nooks’ own product database, ChiliPiper, and their CPQ tool (Subskribe). The ETLs run every few hours via cron jobs, keeping the database current.

On top of that, he deployed Redash, an open-source SQL query tool. Redash sits on top of the Postgres database and lets anyone write queries against it. And every query in Redash has its own API key, which means Claude Code can call Redash, run a query, and return the result.

Josh showed the progression: drag two CSVs into Claude, ask it to match CSM names to invoices. Two minutes, done. Then he realized if Finance needs that report regularly, why is he still manually pulling CSVs? So he automated the entire thing.

Then Josh built a custom MCP server (also hosted on Render) so his teammates could access the database through Claude’s UI. No terminal. No IDE. Just open Claude, connect to the MCP, and type “How many calls did Noah make this quarter that ended in closed-won deals?” Claude queries Redash, joins the tables, and returns the answer. Within 10 minutes, he had a working MCP with OAuth, and his teammate was querying the database in plain English.

He also figured out how to embed Redash reports into HubSpot dashboards (via iframe). So leadership gets their reports exactly where they already look, and Josh can build custom HubSpot reports that wouldn’t be possible to do natively.

The whole thing would have taken a year and a database administrator two years ago. Josh built it during a hackathon weekend with Claude Code.

(By the way, Nooks is productizing a version of this in their new sales engagement platform. I think they should name it JoshBrain.)

• Check out Nooks.

• Read more details (including costs) here: How I built an interactive company brain for our RevOps team.

• Connect with Josh on LinkedIn.

3. How Clarify’s MCP Turns Claude into a Full-Cycle Sales Assistant

(Vinayak Mehta, Software Engineer at Clarify)

Vinayak is a software engineer at Clarify. Clarify is building an autonomous CRM, and they launched their MCP server a few months ago. Vinayak walked through four use cases, all inside Claude chat.

1. Updating a CRM from text message screenshots.

A couple of Clarify’s customers chat with prospects on iMessage. Instead of going back to Clarify to manually enter deal details, they just paste a screenshot of the text conversation into Claude. Claude reads the image, extracts the relevant info, and uses Clarify’s MCP to create the company, person, and deal object automatically.

2. Prospecting and campaign building.

Vinayak typed a natural language query: “Find me VPs of Sales in New York who work at Fortune 500 companies.” Claude used Clarify’s lead finder, found around 2,000 results, imported 10 for the demo, and created a list in Clarify with full contact details including emails. Then he asked Claude to build a 3-step email campaign for those contacts. Claude drafted the campaign, and because Clarify syncs all your sent emails, it matched the rep’s actual writing style.

3. LinkedIn ad retargeting workflow.

This one came from Chris Eberhardt on Clarify’s marketing team. They run LinkedIn thought leadership ads, and every week LinkedIn sends a list of which companies are engaging most. Chris built a Claude skill that takes that list, sends it into Clarify’s lead finder via MCP, finds people (specifically, their personas: founders and heads of sales) at those companies, and enrolls them in an outbound campaign. Automated intent-to-outreach pipeline. This is a killer ABM play.

4. Parsing contracts to update deals.

After closing a deal, a rep pastes the contract PDF into Claude. Claude reads it, extracts the deal amount, contract start date, signer name, job title, and uses Clarify’s MCP to update all the deal fields automatically. No manual data entry.

Across all four, the pattern is the same: Claude does the thinking, Clarify does the CRM work, and the rep never has to leave the Claude chat window.

• Try Clarify (it’s free) at Clarify.ai. Add the MCP via Claude’s connectors (Settings → Connectors → Browse → search “Clarify”).

• Connect with Vinayak on LinkedIn

4. How Attention Auto-Clusters Churning Prospects and Triggers Targeted Outreach with Claude Code

(Anis Bennaceur, Co-founder & CEO at Attention)

Anis is the Co-founder & CEO of Attention. His thesis: the best outbound intelligence is already sitting in your call recordings (and emails/slacks). Most teams just don’t know how to extract it.

He showed a workflow that starts with one prompt in Claude Code: search Attention’s MCP for discovery, demo, and intro calls from the past 90 days. From those conversations, Claude extracts why prospects are evaluating Attention, what tools they’re coming from, what their compelling events are, and what specific language they use to describe their pain (“your best messaging is not written, it’s found”).

That output gets pushed to internal markdown files (Anis calls it his “second brain”), which Claude then uses to build prospect clusters. Not basic ICP segments. Actual persona pockets with names Claude generated on its own (so cool to see!).

He showed two of them.

• Pocket 1: “The RevOps Builder at a Scaling SaaS Company.”

• Pocket 2: “The Frustrated Veteran.”

These aren’t arbitrary labels. Each pocket includes real examples from past calls, the exact pains those people described, the value prop that resonated, targeting criteria (role, seniority, headcount range) and recommended messaging.

Claude then pushes these clusters into Clay’s connector to enrich the data further: firmographics, growth signals, years of experience, company characteristics. The result is a set of 5–10 hyper-targeted persona pockets, each with its own outreach playbook.

The workflow runs on a schedule. Every day, Claude Code pulls the latest calls from Attention’s MCP, looks for new patterns, updates the persona clusters, and sends a Slack notification with any new learnings or outbound ideas. Anis reviews them in the morning and shares the best ones with the team.

He shared this is how you run outbound experiments that get a K-factor above 1. Every new customer conversation generates new intelligence. A fraction of that intelligence produces a new insight. That insight creates a new micro-campaign. That campaign generates new customers, who generate more conversations.

Attention’s MCP isn’t just a raw transcript dump. Their “superagent” batches analysis across 25 calls in parallel, structures the output for LLMs, and surfaces patterns that a basic call recorder MCP can’t. The quality of the MCP matters just as much as having one. They’re truly building an AI-native GTM product.

• Learn more about Attention at Attention.com.

• Connect with Anis on LinkedIn.

5. How WorkSpan Built a Headless Context Layer so Reps Get Everything They Need Directly in Claude Chat

(Sam Gong, SVP Marketing at Workspan)

WorkSpan is a complex product with a complex buyer. Sam’s team had been experimenting with AI for two years: N8N flows, Clay tables, Claude projects, OpenAI agents. Every deployment required loading context. And that context went stale almost immediately.

Sam looked at Octave (a context layer tool) a year ago and passed. It was a dictionary of objects: products, segments, personas, use cases, competitors, proof points. Filling it out would take weeks of manual work.

Then Octave added an MCP.

Sam got on a one-hour call with Octave’s team, opened Claude Code, uploaded three days of SKO transcripts and workshops, and the MCP auto-populated about 60% of Octave’s objects. The tool went from “I’ll never set this up” to “we can’t live without it” in a single session.

Now Octave is WorkSpan’s shared context layer. Sam updates it from Claude after every product meeting, every positioning change, every new competitive development. One prompt: “Hey Claude, we just released a new integration. Update Octave.” The MCP handles the rest.

Sam showed an account plan for Snowflake that Claude generated using Octave. It identified four potential sales plays, ranked them by relevance, pulled in specific reference customers for each play, and flagged an AWS migration mandate that’s time-sensitive. No rep would have assembled that on their own.

The same Octave context then flows into pipeline reports, rep onboarding, and prospecting flows. Update it once, and it compounds across every AI-powered surface in the org. Sam said it best:

“I would not have bought this tool without the MCP.”

• Try Octave (for free) at OctaveHQ.com.

• Connect with Sam on LinkedIn.

6. How 25 Agents in Slack Run MintMCP’s GTM Org, Built with Claude Code and Custom MCPs

(Jiquan Ngiam, Co-founder & CEO at MintMCP)

Jiquan is the Co-founder & CEO of MintMCP. His team is five people. One AE driving seven-figures in pipeline. They’re running a 1-to-5 human-to-agent ratio across the entire business.

His setup was the most advanced architecture anyone showed. Claude Code runs in a sandbox, connects to all their MCP services, uses GitHub as a long-term memory system, and interfaces with the team through both Slack and scheduled tasks that run on a heartbeat (meaning the agents wake up on their own, do work, and report back).

He showed two agents live in Slack:

• Charlie is their call analysis bot. After every sales call finishes, Charlie pulls the transcript via MCP, runs it against Gong best practices that JQ loaded in, and posts a coaching breakdown in Slack. Notes, next steps, what went well, what to improve. No one has to go into a dashboard. It just appears in the channel.

• Peter is their procurement agent. Peter wakes up every morning and every evening, reviews every active deal, checks every open ticket, and pings the humans (including JQ) with reminders. “Hey, this deal needs attention. Hey, this ticket hasn’t been updated.” JQ joked that sometimes he’s not sure if the AI reports to him or if he reports to the AI.

He also showed a live demo of a Claude workflow that pulls recent LinkedIn (he used me as the example, which was fun), identifies who’s engaging with those posts, cross-references against their ICP, and outputs a Google Sheet of prospects to reach out to. All running through custom MCPs for LinkedIn data, Google Sheets, and their CRM.

Jiquan’s team has 25 agents running across engineering, sales, marketing, and prospecting. They use GitHub as the memory layer, which means agents remember what they did yesterday, last week, and last month. You can train them just by talking to them in Slack: “Do this differently next time.” And they will.

The governance piece is important, too. Jiquan’s company (MintMCP) helps enterprises connect Claude to internal systems with proper auth, permissions, and tool-level access controls. For example, their Attio MCP bundle is read-only. Agents can pull deal data but can’t write to the CRM without human approval. That kind of fine-grained control is what makes the difference between “cool experiment” and “I trust this to run at 6 AM while I’m asleep” at a large company.

• Learn more about MintMCP at MintMCP.com.

• Connect with Jiquan on LinkedIn.

As I watched the six different presentations, there was one clear pattern to me:

1. Connect your data to Claude (safely) → 2. Let it think → 3. Automate what works → 4. Move on to the next idea.

In six months, every serious GTM tool will have an MCP. The ones that don’t will get left behind. And the reps and ops people who are building AI-native workflows—like these six—are going to start lapping everyone else.

Let me know if we should do more of these types of live events.

As always, thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Related resources:

• 9 Lessons From 11 Growth-Stage Companies That Built GTM Agents In-House (Vercel, Ramp, LangChain, ClickUp, Deel, Vanta & others: what they built, their tech stacks, and what we can learn from them)

• 6 Ways to Modernize Your GTM Motion in 2026 (Lessons from working with 14 Seed-Series C companies in 2025)

• The GTM Tech Stacks of the Fastest-Growing Private B2B Companies (Research Report)