From MVP to Enterprise Platform: Breeze ML’s Product Evolution Strategy

Building enterprise software isn’t just about adding features – it’s about understanding complex organizational needs and regulatory requirements. In a recent episode of Category Visionaries, Breeze ML founder Harry Xu revealed how they transformed academic expertise into an enterprise-ready AI governance platform.

Starting with Deep Domain Knowledge

Unlike many startups that begin with a minimal product and iterate, Breeze ML started with deep domain expertise. “We are both, I would call, sort of atypical academics who care a lot about impact producing impact than producing papers,” Harry explains. Their technology was already proven in major enterprises: “I worked at Microsoft… Robbie has his technology in products at Netflix and Google.”

Building for Enterprise Requirements

The team recognized that AI governance wasn’t a simple checkbox feature – it required deep integration into development workflows. “Developers are using our tools on a daily basis as they are developing models and transformation of the data sets,” Harry notes. “Our tool provides governance and allows compliance officers and stakeholders of company to quickly gain insight and apply policies over the entire pipeline.”

Finding the Right Early Adopters

Rather than trying to serve everyone, Breeze ML focused on sectors with immediate needs. “Healthcare is the industry that is facing regulations from FDA… The banks are facing very strict regulations and compliance from SEC,” Harry explains. These regulated industries provided ideal testing grounds for their enterprise features.

The Enterprise Sales Reality

The team understood that enterprise sales required patience and persistence. “We’re doing enterprise sales, first of all. So enterprise sales take much longer than the other type of sales,” Harry notes. “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.”

Building Ahead of Market Requirements

Instead of just meeting current needs, Breeze ML built for upcoming regulatory requirements. “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 shares. The stakes drive enterprise adoption: “We’re talking about like a huge fine, something like 6% of your annual global revenue, like uncapped.”

The Challenge of Market Education

Even as they built enterprise features, Breeze ML faced the challenge of educating the market. “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 admits. This required extensive customer discovery: “I just talked to a lot of people. I had tons of conversations with people doing different things in different roles.”

Looking Ahead

The product evolution strategy is positioning Breeze ML for significant growth. “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,” Harry envisions. The goal is clear: “we’ll be the leading platform in AI governance for both the US and EU market.”

For founders building enterprise products, Breeze ML’s journey offers valuable lessons. Success requires more than just technical expertise – it demands deep understanding of enterprise workflows, regulatory requirements, and the patience to navigate long sales cycles. Sometimes, the best product strategy isn’t to build what customers are asking for today, but what regulations will require them to have tomorrow.

This methodical approach to product development, combined with strategic sector focus and deep domain expertise, provides a template for founders building enterprise software in emerging categories. While it might take longer than a rapid MVP iteration cycle, it positions you to build solutions that can truly meet enterprise requirements.