Reframing Sales as Search: Connor Heggie's AI-Powered Approach to GTM at Unify
From Listicles to GTM: The Origin Story
Before Connor Heggie was building AI-powered go-to-market engines, he was working on something that sounds deceptively simple: AI-generated listicles. You know the type—“The 10 Best Non-Stick Pans” or “Top-Rated Tall Shirts for People Over 6‘5”.”
But here’s where it gets interesting. While building this side project in 2022, Connor stumbled onto something profound: the ability to extract meaningful insights from massive amounts of unstructured data.
He was mining reviews, podcasts, and forum discussions—places where real customer problems hide in plain language. A reviewer might write, “I bought this shirt for my husband who’s really tall, and it actually fits him,” and buried in that sentence is valuable semantic information that traditional product databases would never capture.
“I fell in love with that problem space,” Connor recalls. And when he met Austin, his now co-founder who was working on growth automation at Ram, they realized the same principle could revolutionize how salespeople find customers.
Reframing Sales as a Search Problem
Here’s the mindset shift that everything hinges on: Sales isn’t about rhetoric. It’s about finding the right people.
Before starting Unify, Connor admits he had the typical engineer’s bias: “Yuck, sales.” He thought the job of a salesperson was to convince people to buy things. That’s not it at all.
The real job? Find people and companies that have a problem your product uniquely solves. Everything else—the messaging, the communication, the credibility—flows from that foundation. As Connor puts it: “The economy is just us solving each other’s problems for money.”
When you reframe sales as a search problem, suddenly a lot of interesting possibilities open up. Historically, this search was done by humans—it was a system of people. But with modern LLMs, AI, and software engineering, you can build a much better system that offers:
- Repeatability - Same quality every time
- Observability - You can see what’s working
- Scalability - You can reach more customers without proportional overhead
That’s the thesis Unify was built on.
Building the MVP: Speed vs. Scalability
Unify launched in January 2023, just weeks after ChatGPT shook the world. Connor and his co-founder Austin assembled an exceptional team—two of the best engineers Connor had ever worked with—and they had a clear vision of where they wanted to go long-term: web intent-based automated outbound.
The idea: When someone visits your website, reverse IP lookup tells you what company they’re from. You identify relevant buyers at that company, personalize AI-written outbound messaging based on their company attributes and online signals, and send it to their CRM. Automated, intelligent, and scalable.
But they didn’t ship that. Instead, they shipped something simpler: a CSV upload tool that could find people at companies and write prospects to Salesforce. Two and a half months of work for a minimal scope product.
The Unconventional Trade-off
Here’s where Connor’s decision-making gets interesting—and honestly, a bit controversial. Most advice says: build scrappy, ship fast, iterate. If you’re not embarrassed by your MVP, you shipped too late.
Unify did the opposite.
Rather than cutting corners on technical debt, they invested heavily in building a deeply scalable system from day one. They built best-in-class Salesforce integrations. They invested in deep API integrations with data enrichment providers. They built for scale before they had scale.
“It probably delayed us by a month or so,” Connor reflects. “Maybe we could have gotten something out in three weeks instead of two months.”
But then their first customer came on board with 5 million records in their Salesforce instance. Data was updating every 15 minutes at high frequency. And Unify’s system handled it flawlessly because they’d built for that scenario from the beginning.
“We were very glad we had built it to be a highly scalable system,” Connor admits.
This wasn’t obvious at the time. But it worked because Connor and Austin had unusual conviction about the product. Austin had years of growth experience. They had early customers who wanted this. The signals were strong enough to justify the upfront investment in engineering rigor.
The AI Won’t Replace Your Salespeople
Fast forward to 2025, and there’s been a lot of noise about AI replacing sales teams. Connor’s seen the opposite happen.
“We actually want more salespeople,” he says. The pattern mirrors what happened with software engineers: when they get AI tools, they become twice as productive. One plus one equals three. And when you have a salesperson that’s twice as productive, you don’t fire them—you grow your business faster and hire more of them.
The goal with Unify isn’t to automate salespeople. It’s to automate the go-to-market engineering work that happens around them. It’s about giving every salesperson access to a go-to-market engineer and a RevOps expert in their pocket, all the time.
The Bigger Picture
What Connor and the Unify team are building represents a fundamental shift in how modern sales operates. By combining multi-agent AI systems with deep domain expertise in CRM, data enrichment, and sales workflows, they’re creating a new category: the AI GTM engine.
It’s not magic. It’s not replacing humans. It’s about taking the search problem—finding the right people with the right problems—and solving it with the tools we have now. Tools that are exponentially better than they were even a year ago.
For founders and sales leaders, the implication is clear: the future belongs to teams that embrace this shift. Not by cutting salespeople, but by making them smarter, faster, and more focused on what they do best—building relationships with customers who actually need what you’re selling.
Ready to Dive Deeper?
Connor’s story—from side projects to building an AI-powered GTM platform—is packed with lessons on MVP strategy, technical decision-making under uncertainty, and how to think about the intersection of AI and sales.
Listen to the full episode to hear more about Unify’s journey, the infrastructure challenges they’ve tackled, and Connor’s perspective on what’s next for AI in go-to-market.
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