How Breeze ML Built Their First 10 Enterprise Customers Without a Defined Market Category

Building in an undefined market is like selling a solution to a problem people don’t yet know they have. In a recent episode of Category Visionaries, Breeze ML founder Harry Xu revealed their playbook for acquiring early enterprise customers in the nascent AI governance space.

Starting with Regulated Industries

Rather than trying to serve everyone immediately, Breeze ML identified sectors where the need was most acute. “Healthcare is the industry that is facing regulations from FDA,” Harry explains. “The banks are facing very strict regulations and compliance from SEC. For example, there are a lot of laws and regulations regarding how your model should be trained, how you should collect user data.”

This focus on heavily regulated industries provided natural early adopters. These companies already understood the importance of compliance and had existing frameworks for evaluating governance solutions.

Deep Customer Discovery

Instead of rushing to build features, Breeze ML invested heavily in understanding their potential customers’ needs. “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 critical insight: while everyone recognized the need for AI governance, nobody knew exactly how to implement it. “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.”

Building for Future Requirements

The team positioned their solution ahead of upcoming regulations. “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,” Harry notes. The consequences of non-compliance created urgency: “We’re talking about like a huge fine, something like 6% of your annual global revenue, like uncapped.”

The Enterprise Sales Approach

Breeze ML adopted a methodical approach to enterprise sales, recognizing that the sales cycle would be longer in an emerging category. “We’re doing enterprise sales… enterprise sales take much longer than the other type of sales,” Harry explains. “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.”

Their strategy focused on building a robust sales pipeline rather than rushing for quick wins: “What we’re doing right now is that we’re building a sales pipeline. And I hope that once the sales pipeline is built, we can see a very rapid growth in next year or next couple of years.”

The Power of Academic Credibility

Unlike typical startups, Breeze ML leveraged their academic background to build credibility. “Both of us had a lot of experience with pushing technology into the actual products for large companies,” Harry notes. Their track record included successful implementations at major tech companies: “I worked at Microsoft… Robbie has his technology in products at Netflix and Google.”

This academic-to-enterprise transition helped establish trust with potential customers, particularly crucial when selling to large organizations in regulated industries.

Looking Ahead

The company’s early success in acquiring enterprise customers has shaped their ambitious vision: “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.”

For founders building in undefined markets, Breeze ML’s journey offers valuable lessons: start with sectors where the pain is most acute, invest heavily in customer discovery, position ahead of regulatory requirements, and leverage your unique credibility factors. Sometimes, the lack of a defined market category isn’t a weakness – it’s an opportunity to shape the category itself.