The Story of Breeze ML: Building the Infrastructure for AI Governance

Most startup stories begin with a founder’s “aha moment.” But for Breeze ML, the journey started with decades of academic research and a contrarian bet on the future of AI. In a recent episode of Category Visionaries, founder Harry Xu shared how a distinguished academic career led to building the infrastructure for responsible AI deployment.

From Academia to Entrepreneurship

Unlike typical founders chasing the next big thing, Harry and his co-founder had spent years pushing technology from research into production. “My Co-Founder, Ravi Naturali, who is a professor of computer science at the Princeton University… we are both, I would call, sort of atypical academics who care a lot about impact producing impact than producing papers.”

This focus on real-world impact shaped their approach to research. “Most of projects do not stop at papers. We always go extra miles and open source our tools and try to get people to use,” Harry explains. Their work wasn’t just theoretical – it was already powering major tech companies: “I worked at Microsoft a few years back, and then I worked on an optimizing compiler that I think is still used in production systems… Robbie has his technology in products at Netflix and Google.”

Launching in a Market Downturn

The timing of Breeze ML’s launch in March 2022 presented immediate challenges. “We actually had a lot of challenges of raising because of the market downturn. There was a market crash in May 2022, and we had a lot of issues in the beginning of raising our seed around,” Harry recalls.

The emergence of ChatGPT later that year created both opportunities and challenges. While it sparked massive interest in AI, it also led many companies to focus solely on language models. “Had we waited a few months, I think we would have had a much better sort of situation,” Harry notes.

The Pivot to Governance

While others rushed to build AI models, Breeze ML identified a more fundamental need. “AI regulations are coming our way,” Harry explains. “The European Union, like EU, has been much more advanced in terms of AI legislation than the rest of the world.”

The stakes are enormous: “The consequence of not being compliant is actually huge… We’re talking about like a huge fine, something like 6% of your annual global revenue, like uncapped.”

This realization led to their focus on “governance by construction” – tools that help companies build compliance into their AI development process from the ground up.

Building in an Undefined Market

The challenge they face is unique: building tools for requirements that are still being defined. “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,” Harry notes.

Even experts are still figuring out the specifics: “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.”

The Vision Ahead

Looking to the future, Harry’s ambitions are clear: “We’re aiming to raise our series a next year. So three, five years down the road, I believe that we’ll become a company with several hundred people… we’ll be the leading platform in AI governance for both the US and EU market.”

This isn’t just about building a successful company – it’s about creating the infrastructure that will make responsible AI development possible at scale. “Most of the large companies will be using our platform,” Harry envisions.

The story of Breeze ML represents a different kind of Silicon Valley narrative. Instead of chasing the latest trend, they’re building the foundational infrastructure that will enable the safe and compliant deployment of AI technology. In a market obsessed with what AI can do, they’re focused on how to do it responsibly.