Titan ML’s Pivot Playbook: When to Walk Away from Enterprise Deals

The hardest decisions in company building often involve saying no to money on the table. In a recent episode of Category Visionaries, Meryem Arik shared how Titan ML faced this challenge when a major financial institution showed interest in their AI infrastructure solution.

The Enterprise Deal That Wasn’t

Early in Titan ML’s journey, they encountered what seemed like a dream scenario: a large financial institution wanted their solution. For many startups, especially in the challenging pre-ChatGPT era of AI infrastructure, this would have been an obvious yes. But something didn’t feel right.

“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. Rather than chase the deal, they made the difficult choice to walk away. “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.”

The Market Timing Factor

This decision came during a pivotal moment in the AI landscape. “We had investors telling us that they didn’t think that NLP and language AI would be a big enough market,” Meryem recalls. In this context, turning down a major enterprise deal required even more conviction.

The team’s thesis was clear: “If we think that AI is going to be as widely adopted as we think it is, then we have a lot of work to do, and we can’t have ML engineers at every single business and every single enterprise building the same infrastructure over and over again.”

Building for Scale vs. Custom Solutions

The decision framework came down to a fundamental question: were they building infrastructure or custom solutions? As Meryem explains, “What they really should be focusing on is solving the problems that are unique to their business, making sure the data is in the form they need, and making sure the application is what they need for their business specific application.”

This clarity about their role in the market – building standardized infrastructure rather than custom solutions – helped guide the decision. The goal wasn’t just to solve one company’s problems, but to create something that could scale across the industry.

The Long-Term Vision

Looking back, this decision aligned perfectly with their ultimate 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 shares. “We want it to be analogous to deploying a database when you don’t even really think about it, and it’s just kind of part of your stack.”

Building Trust Through Focus

This focus on solving broader market problems rather than chasing individual deals has shaped their entire go-to-market approach. “What we have found has worked really well with developers is less salesy and more educational piece,” Meryem notes. Their willingness to discuss alternatives and be transparent about capabilities builds credibility in the developer community.

For founders facing similar decisions, Titan ML’s experience offers a crucial lesson: sometimes the best strategic decisions feel like missed opportunities in the moment. The key is maintaining clarity about what you’re building and who you’re building it for, even when saying no to revenue feels counterintuitive.

The broader market has validated this approach. As Meryem observes, we’re now “starting to see businesses have methodologies and ways that they can prove AI value.” By staying focused on the broader infrastructure problem rather than getting pulled into custom solutions, Titan ML positioned themselves to capture this wave of adoption.