From UCLA to Startup: Breeze ML’s Journey from Academic Research to Enterprise Software
Most academics stay in the ivory tower. But what happens when researchers decide their work is too important to remain in the pages of academic journals? In a recent episode of Category Visionaries, Breeze ML founder Harry Xu shared how he and his co-founder bridged the gap between academia and enterprise software.
Breaking the Academic Mold
The typical view of academia is that it’s divorced from commercial realities. But Harry and his co-founder were different from the start. “We are both, I would call, sort of atypical academics who care a lot about impact producing impact than producing papers,” Harry explains.
This focus on real-world impact shaped their entire research approach: “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.”
Building Commercial Credibility
Long before founding Breeze ML, both founders had established track records of implementing their research in 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,” Harry notes. His co-founder’s impact was equally impressive: “Robbie has his technology in products at Netflix and Google. If you open Google’s Chrome browser, you will see his technology running there.”
The Reality of Academic Life
The common perception of academic life as leisurely couldn’t be further from the truth. “A lot of people think that academics are very easy life because you don’t have to produce any product, there’s no sort of monetary aspect to your life because we don’t really talk too much about money,” Harry shares.
The reality? “The life can be quite challenging because we have a lot of projects. We kind of work as a startup, right? Because you have a bunch of students you have to feed. We also have to talk to national funding agencies like National Science foundation or like Office of the Naval Research to secure funds.”
The Transition to Enterprise
When the time came to build Breeze ML, their academic background provided unique advantages. Instead of chasing the LLM hype, they identified a fundamental need in AI governance. Their experience in both academic research and commercial implementation helped them understand the gap between theoretical compliance and practical implementation.
“AI regulations are coming our way,” Harry explains. “The problem a lot of companies are suffering from is they don’t really have any tools, any guardrails that allow them to be compliant as they’re working on the model development.”
Building for Impact
The founders’ academic mindset influenced their approach to product development. Rather than rushing to market with a minimal solution, they focused on building comprehensive “governance by construction tools” that developers could use daily while developing models and transforming datasets.
Their academic experience also helped them understand the importance of rigorous validation. “We clearly saw governance is a bigger problem in years to come,” Harry notes. This insight came from extensive customer discovery, mirroring the thorough research methodology they’d developed in academia.
Looking to the Future
The vision for Breeze ML reflects both academic ambition and commercial pragmatism. “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.”
For technical founders transitioning from research to commercial products, Breeze ML’s journey offers valuable lessons. Success isn’t about abandoning academic rigor – it’s about applying that same methodical approach to solving real-world problems. Sometimes, the best commercial opportunities come from identifying the practical implications of theoretical challenges.
The key is maintaining the academic commitment to thoroughness while adapting to commercial realities. As Harry’s experience shows, the skills developed in academia – from securing funding to managing complex projects – can translate surprisingly well to building a startup, as long as you stay focused on real-world impact.