Breeze ML’s Category Creation Strategy: Defining the AI Governance Market

Creating a new software category isn’t just about building innovative technology – it’s about defining a market that doesn’t yet exist. In a recent episode of Category Visionaries, Breeze ML founder Harry Xu shared how they’re establishing AI governance as a crucial enterprise software category.

The Market Reality

“AI regulations are coming our way,” Harry explains, laying out the fundamental market driver. “The EU AI act is already there, and then they’re looking to finalize the law by the end of this year, and then that’s going to come into effect in the year of 2025.”

The consequences create urgency: “We’re talking about like a huge fine, something like 6% of your annual global revenue, like uncapped.” Yet despite these looming requirements, companies lack the tools to ensure compliance.

Defining the Category Through Customer Discovery

Instead of prescribing solutions, Breeze ML invested heavily in understanding the market need. “I just talked to a lot of people. I had tons of conversations with people doing different things in different roles… data scientists… machine learning engineers… VP of engineering… compliance officers… CTOs, CEOs,” Harry shares.

These conversations revealed a crucial insight: while everyone recognized the need for AI governance, nobody knew exactly what it meant in practice. “People don’t know what to do yet… people in this market don’t know what to do at this point. And there are no existing tools.”

Finding the Strategic Entry Point

Rather than trying to serve everyone immediately, Breeze ML identified sectors where governance was already crucial. “Healthcare is the industry that is facing regulations from FDA… The banks are facing very strict regulations and compliance from SEC,” Harry notes.

These regulated industries provided natural early adopters and helped shape the category definition. Their existing compliance requirements created frameworks that could be adapted for AI governance.

Building Enterprise Trust

Breeze ML leveraged their academic background to establish credibility in this new category. “We are both, I would call, sort of atypical academics who care a lot about impact producing impact than producing papers,” Harry explains. Their track record included successful implementations at major tech companies: “I worked at Microsoft… Robbie has his technology in products at Netflix and Google.”

The ‘Governance by Construction’ Approach

Instead of treating governance as an afterthought, Breeze ML introduced the concept of “governance by construction” – embedding compliance into the development process itself. “Developers are using our tools on a daily basis as they are developing models and transformation of the data sets,” Harry explains.

This approach helps define AI governance not as a checkbox exercise but as an integral part of the AI development lifecycle.

Market Education Challenge

Even experts are still figuring out what AI governance means in practice. “We talked to a lot of lawyers and privacy attorneys… everybody was talking about auditing AI, auditing models. But in terms of concrete steps, the action items, nobody had a good idea of what to audit,” Harry shares.

This knowledge gap creates both challenges and opportunities. While it makes the sales cycle longer, it also allows Breeze ML to shape how the market thinks about AI governance.

The Long-Term Vision

The category creation strategy is already showing results. “We have a few paying customers right now. And then we had a lot of those customers that are trialing our products at this moment,” Harry notes. Looking ahead, the vision is clear: “We’ll be the leading platform in AI governance for both the US and EU market.”

For founders creating new categories, Breeze ML’s approach offers valuable lessons. Success requires more than just building technology – it requires educating the market, finding strategic entry points, and establishing credibility through domain expertise. Sometimes, the opportunity to define a category comes not from technological innovation alone, but from recognizing fundamental market needs before they become obvious to everyone else.