The Platform that is Turning Individual Sellers into GTM Engineers
Koala x The Signal
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I got connected to Tido (Co-founder & CEO of Koala) towards the end of my time running Groundswell. It was early days for Koala, but from my first conversation, I could tell Tido was special. When you talk with him, you feel his energy. He’s kind, optimistic, and curious. Oh, and he’s a beast. He went to Harvard (if you care about that sort of thing), then was an early eng/eng manager at Facebook, then engineer manager at Dropbox, then was at Segment for 6 years as their VP of Engineering (where he met his co-founders, Matt and Netto). Yet, he’s one of the most humble people you’ll meet. Maybe to a fault! I often tell Tido he should be doing more bragging—er, I mean—marketing about what they’ve built. Because it’s epic.
More recently, people like Quinn and Matt joined, who are legit at GTM. And it’s clear to me that there is a vortex of talent forming at Koala (this is the best indicator of a company’s success, that I’ve found to date).
They’re also announcing their Series A raise today! And I know this is only the beginning for this team of tree-dwelling marsupials 🐨.
Anyway, it’s been fun to see their product evolve from a little feature for tracking product users viewing technical docs, to a more exhaustive first and third-party signal platform, into what is now becoming a really powerful platform enabling the creation and use of AI agents (which, I cannot be more bullish on).
I’m excited to share this Sponsored Deep Dive Post on what I find most interesting about what Koala is building.
Here’s what we cover in today’s post:
Intro
Democratizing GTM intelligence
AI agents
Value over volume
Data-driven Plays
Let’s get into it.
The Platform that is Turning Individual Sellers into GTM Engineers
There’s a single question that’s been bouncing around in my head for a while now. It’s something Tido posed when we caught up recently, and I can’t shake it. As I see it, it’s the philosophical question around how big Koala can become:
“Can you make a product that gives the powers of a GTM engineer to any frontline person in the GTM org?”
I see a race unfolding between two different philosophies. I think both will co-exist, but I suspect in a few years, we’ll look back and see that one of them is much bigger than the other.
Path #1 → Enabling reps to leverage and scale their expertise.
Path #2 → Relies on centralized teams, like RevOps and GTM Engineering, to scale the go-to-market motion - using tools like Clay.
This piece lays out the bull case for Path #1, that Koala is helping blaze.
Democratizing GTM intelligence
I’m sorry for using this term. I am sure there is a clip from Silicon Valley that makes fun of startups that use this term, “democratizing xyz.” But, in this case, it’s true.
By combining AI-driven insights with a more intuitive, rep-focused workflow, Koala allows GTM teams to run and iterate on plays in realtime—without needing to be technical.
This approach acknowledges a crucial insight: frontline reps often have the best intuition about what signals matter and what messaging works.
And AI agents are at the core of this motion (and growing quickly).
AI agents
When Tido shared their new AI agent feature with me, he prefaced it by saying: "It's crazy – but crazy in a good way." I like crazy/weird products - if it’s for a purpose (and, in this case, it is).
Koala enables AI agents to be created, shared, and refined by individual reps (SDRs, AEs, AMs, etc.) to do things like: answer specific discovery questions, analyze customer signals, and generate targeted outreach messages.
What sets these agents apart is their ability to leverage both first-party data from tools like Salesforce and Snowflake, as well as third-party signals, creating a broader context window than what's available through general LLMs like Perplexity, OpenAI, Claude, or Gemini. The result is a system that enables reps to conduct deeper, more meaningful research before engaging with prospects.
I’ve thought about this concept for years. Part of the magic of Outreach, SalesLoft, et al., was the fact that an individual rep could create their own sequence, get good results (with data to back it, for all to see), and then another rep could use that sequence too - or, “clone” it and “make it their own.” In engineering terms, this is called “forking” it.
Another concept I always thought should exist is a Git repo, where an engineer can publish their code, but for RevOps/GTM Engineering workflows (or, for individual reps). This way, others can see it and use it. Or fork it. They can also re-use it at future companies (in some cases). Maybe this is a glimpse into that future…
Here are a few examples of ways Koala’s AI agents can be used:
Dynamic prospect research: AI surfaces relevant company and contact insights instantly.
Message writing: AI suggests copy based on signals.
Real-time collaboration: Reps can share and improve agents as a team, creating a living, evolving sales knowledge base.
Value over volume
One of Koala's core principles is flipping traditional sales metrics on their head. Instead of focusing on volume—sending hundreds of emails to get a single meeting—the platform encourages a more thoughtful, research-driven approach. "When someone gets Koala, they just start generating pipeline," Tido told me. "It's like having a go-to-market engineer with an unfair advantage."
This shift requires sales reps to think differently about their role. Rather than operating in "cold calling mode," they enter what Tido calls "support mode" – taking the time to understand every way an account has interacted with their company and combining it with custom enrichment data to create highly relevant outreach.
I love this concept. I’ve talked about this a bunch – that the best salespeople are “Sherpas” (guides) who genuinely help prospects solve their problems (often, by using the solution they ‘sell’).
By putting more data and insights directly into the hands of reps, Koala is betting that the future of sales will be more targeted, more efficient, and ultimately, more human.
I think this wild game of high-volume, domain buying + mailbox warming/rotating is going to be short-lived. It’s just not sustainable. Google/Microsoft can shut it down any time they want.
In my (strong, but humble) opinion, the future of go-to-market is lower volume but much higher conversions, by running motions like micro-campaigns. (Fun fact, I remember when I first started talking about micro-campaigns, Tido told me he had also been talking about this - calling them “Smart Campaigns” which I love(d)).
Tido sums it up nicely:
“The teams that stop spinning up domains and sending 10,000 emails and instead adopt a more thoughtful, AI-driven approach are the ones that will win.”
This is Koala’s big bet—to build the tooling for individual reps to run these plays into their book of business, without requiring Ops or a GTM Engineer.
Data-driven Plays
The tooling to answer “What plays are working today?” is hacky at best, and non-existent at worst. Because data is scattered across multiple platforms. AI only makes this problem worse. I felt this pain first-hand when I was running 85+ concurrent (fully automated) plays at Apollo, and only had sequence-level visibility. No forecasting. No ability to mash up multiple signals into a single play.
Koala has built elegant Play reporting. It tracks which signals and approaches lead to conversions, providing detailed analytics on play performance. For instance, one of their customers was able to use Koala to see that accounts viewing their documentation security pages converted at 13.7x higher than average homepage visitors, representing millions in pipeline opportunity.
This level of insight allows GTM teams to focus their efforts on the most promising opportunities. "It's your cheat sheet of what plays you should even be setting up," Tido said, as he walked me through how it helps teams identify and prioritize the most effective engagement strategies.
You’ll notice the Plays are also for several use cases throughout the lifecycle of the customer. Everything from pipe gen (SDR), to deal management (AE) through to post-sales (CS) with expansion and churn prevention plays. Koala is starting to move horizontally across the go-to-market org. For what it’s worth - I believe these roles are going to collapse (blur) into each other in the coming years, especially at PLG companies. So, if that happens, it’ll be a great tailwind for them.
Koala is building three pillars to enable more intelligent and data-driven Plays:
Trigger + Audience + Assignment = Play Execution
AI-driven discovery to suggest the best plays to run
Real-time reporting on play performance
Instead of manually creating complex sequences, Koala enables sales reps to leverage AI to generate messaging that’s dynamically optimized for every lead. This removes the bottleneck of GTM engineers while allowing reps to focus on what they do best—building relationships and closing deals. This is right in line with how I think about Sequences 2.0, which I wrote about recently.
This shift is critical because it allows for a new kind of efficiency:
Instead of sending 100 emails to get one meeting, Koala aims for a 10:1 or even 1:1 ratio by increasing message relevance.
AI optimizes each touchpoint based on historical play success rates.
Sales reps get insights into what’s actually working, rather than relying on trial and error.
As companies continue to look for more efficient ways to scale their go-to-market motion, I think Koala's approach of combining AI capabilities, first-party data, and rep’s intuition is a sneak peek into the modern go-to-market playbook.
I’m excited to continue watching Tido and the team help build the infrastructure that will run the more intelligent and human-powered go-to-market playbook of the future.
If you have reps who are looking to improve their workflows by integrating intelligent signals and AI agents, you should check out Koala (they have a free version of their product—sign up for free today).
Thank you for your continued attention and trust — I do not take it for granted.
See you next week,
Brendan 🫡
Interesting, I will try it. One topic that I haven't read enough about is how to get a proper ICP, because based on that all these efforts make sense, and sometimes it's not easy to connect these ICP features with the signals you can map from these tools.