5 Critical Go-to-Market Lessons from Titan ML’s Journey in AI Infrastructure

Technical founders often struggle with the transition from building to selling. In a recent episode of Category Visionaries, Meryem Arik, founder of Titan ML, shared hard-earned insights about bringing AI infrastructure to market during one of the most turbulent periods in tech history.

Here are five crucial GTM lessons from their journey:

1. Early Market Validation Beats Perfect Product

The most expensive mistake technical founders make isn’t building the wrong thing – it’s building too much of anything before validation. “Build less and sell earlier,” Meryem emphasizes. “It’s a mistake that we made before we raise money is we would build too much. We’ve now gotten very disciplined of we don’t build really much at all, if anything, unless we have tried to sell it and seeing the reaction we want to be seeing.”

2. Market Timing Changes Everything – But Not How You Think

Titan ML started with a thesis about deep learning’s future before ChatGPT existed. “We had investors telling us that they didn’t think that NLP and language AI would be a big enough market,” Meryem recalls. But when ChatGPT launched, the challenge wasn’t proving market size – it was helping confused buyers. “I think what we found in the beginning half of 2023 is that businesses wanted to, quote unquote, ‘do AI,’ but hadn’t figured out how to do it. So there was just like a bit of a, almost a panic.”

3. Choose Your Category Before It Chooses You

In emerging markets, category definition becomes a strategic imperative. “The category is really still to be defined, and we’re part of setting up that niche, and there’s only a couple players playing in that niche, and we’re all working to define what that category looks like,” Meryem explains. This isn’t just marketing – it’s about shaping how buyers understand and evaluate solutions.

4. Developer Marketing Requires Radical Authenticity

Selling to technical teams demands a different approach. “What we have found has worked really well with developers is less salesy and more educational piece,” Meryem shares. “We are very happy to talk to our clients about open source alternatives to what we’re doing or how they can build it themselves or the underlying technology underneath it. Because if they find that content useful, then we gain trust and we gain credibility.”

5. Scale Through Systems, Not Heroes

The hardest part of growing isn’t getting those first customers – it’s building repeatable systems that don’t depend on founders. “I’m heavily involved in every single sales cycle,” Meryem notes. “So my co-founders and I would like to get to the point by the end of the year where we’ve defined the sales process well enough that in theory, although probably not in reality, we could take a step away from that and really grow that much more.”

Sometimes the best strategic decisions mean walking away from revenue. When faced with a large contract opportunity from a major financial institution, Titan ML made the difficult choice to decline. “We realized that their use case was quite unique to them, and that was a huge contract that I guess would have left on the table,” Meryem reveals. “We did a lot of reflection and decided that weren’t going to go down that avenue of building what they wanted, but actually were going to go down the avenue of building what we thought the wider problem in the market was.”

This ability to say no – to custom features, to major contracts, to tempting distractions – ultimately shapes the go-to-market motion. It’s about building something that can scale beyond any individual customer or team member.

For technical founders navigating similar challenges, these lessons highlight a crucial truth: the path from technical insight to market success isn’t just about building the right product. It’s about building the right go-to-market engine that can take that product to scale.