From Technical Insight to Market Success: Titan ML’s Framework for Product Validation

Technical founders often fall into a common trap: building first, selling later. In a recent episode of Category Visionaries, Meryem Arik shared how Titan ML flipped this paradigm, developing a rigorous approach to market validation that precedes any significant engineering investment.

The Build Less, Sell Earlier Philosophy

The core insight came from hard-earned experience. “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.”

This wasn’t just about avoiding wasted engineering effort. As Meryem puts it bluntly: “Building is easy and finding things that people want to buy is hard.”

Identifying Real Market Problems

Their validation process starts with understanding the fundamental problems ML teams face. “ML engineers are spending way too much of their time on building infrastructure rather than solving the problems that are really core to their business,” Meryem explains. This insight shaped their entire approach to product development.

When potential customers express interest, Titan ML doesn’t immediately start building. Instead, they engage in deep technical discussions. “What we have found has worked really well with developers is less salesy and more educational piece,” Meryem notes. “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.”

The Enterprise Test Case

One of their most important validation lessons came from a potential enterprise deal. “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 shares. Rather than build custom features, they made the difficult decision to pass. “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.”

Evolving with Market Maturity

The validation process has evolved as the market has matured. “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,” Meryem recalls. “Now we’re starting to see businesses have methodologies and ways that they can prove AI value.”

This market evolution required adapting their validation approach. Instead of just proving technical feasibility, they needed to help customers understand how to measure success.

Building for Long-Term Scale

Every feature decision is evaluated against their long-term vision. “We would like Titan ML infrastructure and Titan ML technology to be a core and probably invisible part of almost every single LLM deployment and generative AI deployment,” Meryem explains. This means saying no to features that might drive short-term revenue but don’t align with becoming fundamental infrastructure.

For technical founders, Titan ML’s framework offers a clear lesson: the path to product-market fit isn’t about building the perfect product first. It’s about validating market needs through sales conversations, even when – especially when – you don’t have everything built yet. As Meryem’s experience shows, sometimes the most valuable engineering decision is choosing not to build at all.