The AI Research Company Building GTM Superintelligence
Actively AI x The Signal
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Hey y’all!
If you've been following my content lately, you know my stance: despite the hype, AI is still seriously underrated—especially when it comes to go-to-market teams.
I’ve spent hours talking with founders and GTM leaders, and one thing’s clear: the market is crowded with AI sales assistants that are mostly just prettier "if-then" logic machines focused on increasing volume rather than driving precision.
What really caught my attention about Actively AI was hearing multiple GTM leaders—from companies like Ramp and Navan—unprompted, enthusiastically highlight the outcomes Actively delivers, rather than just talking about the AI itself. So, naturally, I got curious.
Then I met Mihir and Anshul, the founders. These aren't marketers who just slapped "AI" on their product; they're Stanford-trained AI researchers. They’ve built something fundamentally different—a platform they call "GTM Superintelligence". Instead of generic email automation or signal overload, Actively deeply reasons through your data, continuously learns your business nuances, and systematically gets smarter over time.
After spending time with the team, I’m excited to share this Deep Dive into what makes Actively AI special—and why it genuinely stands apart in the crowded AI-for-sales space.
Oh, and they just announced a fat new round of funding yesterday ($22.5M), so the big-brained, patagonia vest wearers see something exciting about them too (it’s not just me —a pontificatoooor—who thinks they’re up to something big).
Here's what we cover in today's post:
What is “GTM Superintelligence?”
How Reasoning Beats Signal Overload
Actively Learning: The System that Gets Smarter with Time
Practical Use Cases that Drive Revenue
Looking Forward: The Future of Go-to-Market Intelligence
Let's dive in.
What is “GTM Superintelligence?”
The majority of the current wave of AI sales tools aren’t solving the biggest GTM problem (imho).
The most successful sales reps aren't the ones sending the most emails or making the most calls. They're the ones who are incredibly precise—targeting the right accounts, identifying the right people, developing crisp hypotheses, and engaging at the right time.
It’s a tale as old as time. Quality > quantity. My first boss taught me this lesson over Skype (RIP) over a decade ago. Yet, people and systems have not learned to put this best practice into, well, practice.
And AI only exacerbates this problem. Hitting “more” has never been easier with AI. So, how do we use AI to finally build a practice around quality over quantity?
Actively AI is creating a new category called "GTM Superintelligence." They define it as AI that can reason through and optimize across complex sales data significantly better than humans can. It's not about automating tasks; it's about creating intelligence that gets better at driving revenue over time. In other words—not only increasing productivity on a per-rep basis but doing it in a predictable fashion. (It’s basically a smarter version of what I’ve recently written about in the past, what I called, The "System of Intelligence".)
The go-to-market formula for the last five years has been "more input equals more output.” Whether that’s more signals, more emails, or more calls. That formula is broken now because we've gone too far.
Today, the majority of this knowledge sits in the heads of the best sellers. There’s literally a term for this: “institutional knowledge.” I’ve thought about this problem a lot. Until now, no software has captured and passed on the judgment of top reps. When they get promoted or move on to a new company, their playbook usually leaves with them. Actively changes that.
How Reasoning Beats Signal Overload
Modern B2B go-to-market isn’t simple anymore. It’s multi-product, multi-persona, and multi-threaded—especially in large organizations. When I was leading Ops & Enablement for the BDR org at Zoom Video, we could sell seven different products to five different personas within a single account.
At each step of the sales process, you need to reason through which direction to take: Which product should I lead with? Which persona should I target? Who is already using the product? What's the right hypothesis based on their job postings/10Ks? What about our past conversations? Is there a better time to engage them?
These aren’t “checklist” decisions—they require reasoning. Mihir, Actively’s co-founder, calls this a high-cardinality, high-dimensionality problem. In ML terms, that means you’re dealing with a huge number of variables and possibilities—far too complex for simple rules or static playbooks. It’s where humans start to guess—and where machines—if built right—can actually outperform us.
But the existing suite of tools in GTM today aren’t built to reason. Take signal-based platforms. They follow simple “if/then” logic. Something like: "If someone visits the website, then enroll them in X sequence."
Repeat that across 100 different signals, and you get noise—lots of motion, little strategy. These tools don’t ask: Which account actually matters? Which contacts have real intent? What initiative are they driving? What do similar buyers care about? How do previous attempts to engage this account inform future ones?
The best reps don’t just react to signals alone. They reason—through data, patterns, past experience, and intuition. They’re selective. They build tight hypotheses. They prioritize deeply. They might not send the most emails or make the most calls, but their outreach leverages intent and precision.
Actively turns that approach into software and then takes it to the next level. It builds a custom reasoning engine for each customer—trained on your ICP, your GTM motion, your data, and your product set. It connects to your systems and reasons through them like your best reps would, but faster and at scale. And most importantly, it’s trained to optimize for one thing at every step of the process: revenue.
Anshul shared an analogy with me. He said to think of it like the transition from early search engines to Google. Yahoo and MSN had search, but they treated all keywords equally. Then Google introduced PageRank—a system that mined through the web’s link structure to find what actually mattered to users. It didn’t win with a better UI. It won by solving the right problem.
That’s what Actively is doing for GTM. Not just selling more signals. Making sense of them—to drive results.
Actively Learning: The System that Gets Smarter with Time
One of the most compelling aspects of Actively is right in the name—the platform actively learns over time. “Actively" stands for "active learning"—a concept from the founders' research background at Stanford.
Most GTM tech gets worse the more you use it (looking at you, Salesforce...). After several years of usage, your CRM is bloated with fields, broken workflows, and a graveyard of reports. The data piles up, but the system doesn't get any smarter (and, it gets slower aka worse).
Actively flips this on its head. It continuously learns from closed-won data and the factors that predict success across the lifecycle of your funnel. It gets trained both explicitly (when you tell it about your product lines and personas) and implicitly (as it observes patterns in your business). This is the type of GTM tech magic that I’ve always dreamed of.
This matters especially for fast-growing companies and enterprises with nuanced outbound motions. Take Ramp, for example—they’re constantly launching new products, which means the complexity of their GTM motion has to evolve just as quickly. Actively continuously evolves and has helped Ramp increase win rates by 23%.
And again, when sales reps turn over (as they inevitably do), all that knowledge isn't lost. The system retains the learning and passes it on to the next rep, to help each new rep achieve those sweet, sweet quota-attainment goals (and ramp much quicker).
Practical Use Cases that Drive Revenue
Alright, I’ve yapped about a bunch of theoretic stuff, and machine learning terms that I had to double-check with my boss (ChatGPT) to ensure I was using them correctly. But, what does this actually mean in practice?
Quick note: the bigger the company, the more data it has. And the more data, the better Actively AI performs. So if you’re a small sales team, this probably isn’t the solution for you.
Here are a few specific ways companies are deploying Actively to leverage structured and unstructured data across all 1st-party and 3rd-party systems, to optimize for revenue:
Targeted Outbound
Each morning, Actively AI analyzes your entire territory to pinpoint the top contacts and accounts most likely to convert across millions of internal & external data points. For each account, it provides hypotheses on why and how to engage, a strategy for multi-threading across recommended contacts, relevant messaging, sequence selection, and personalized call scripts.
As Anshul explained, “What if you could assign 1 AE to each account in your TAM? The depth with which they’d look to research, understand, and constantly figure out how to best engage that account is what we aim to replicate with AI.”
Increasing Predictability
Actively learns what personas, messages, and sequences convert—then serves up recommendations reps actually use. That means you can control (and increase) the quality of pipeline created each day, while also having predictable visibility into how it's happening.
Maximizing TAM Coverage
Most companies sit on a goldmine of unassigned or marketing-owned accounts that never get touched. Actively’s dynamic scoring system re-evaluates that pool daily, surfacing leads who quietly match the pain points your product solves—even if no one’s reached out recently.
Accelerating New Product Launches
When launching a new product, identifying the right prospects quickly is crucial. Instead of looking for exact keyword matches, Actively reasons through past interactions—calls, emails, RFPs—as well as external research indicating interest in related solutions, to engage the right accounts with precisely timed messaging from day one of a new launch.
AI-Enabled Campaigns
Actively bridges the gap between a marketing idea and sales execution. Say you want to run a campaign targeting customers using a competitor. With Actively, you can mine all your interactions—from cold call dispositions to email responses to sales call recordings to job descriptions mentioning the competitor—giving you an exhaustive view of all the competitors in your space, when renewals could be coming up, and ways to position against them.
Seamless Integration
Actively plugs directly into your sales stack—pushing intelligence into your CRM or outreach tools. No new tabs. No extra training. Just reps spending more time on high-quality conversations and less time fiddling with software.
Looking Forward: The Future of Go-to-Market Intelligence
We're in the early days of a fundamental shift in how AI will impact go-to-market.
The first wave of AI brought us tools focused on more emails, more calls, and more signals.
The second wave is bringing us copilots that help augment individual tasks.
But the third wave—the one Actively AI is pioneering—is about creating a GTM Superintelligence that can reason through complexity and optimize for revenue outcomes across your entire GTM motion.
This trajectory is similar to what is happening in the broader AI ecosystem. We've moved from models trained on more data, more compute, and more layers to systems focused on reasoning deeply. So, it's not about volume anymore, it's about depth. GTM is no different.
I’m excited that a platform like Actively AI is actually focused on precision over volume. Which is better for both buyers and sellers. I can’t wait to see how they continue to push the boundaries of what is possible with AI, to create a more efficient marketplace for buyers and sellers.
And building something this advanced takes a rare mix: world-class engineers, seasoned GTM operators, and deeply technical AI founders. Actively has that team.
If you're serious about driving real revenue growth through intelligent, precise GTM, Actively AI is worth checking out.
Thank you for your continued attention and trust — I do not take it for granted.
See you next time,
Brendan 🫡
great piece! how are you guys different from Pocus?