The Story of Fero Labs: Building the Intelligence Layer for Industrial Manufacturing

From Columbia PhD to industrial AI pioneer – how Fero Labs is transforming manufacturing with explainable AI and profitable sustainability solutions.

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The Story of Fero Labs: Building the Intelligence Layer for Industrial Manufacturing

The Story of Fero Labs: Building the Intelligence Layer for Industrial Manufacturing

Most PhD students dream of becoming professors. Berk Birand had a different vision – using AI to revolutionize industrial manufacturing. In a recent episode of Category Visionaries, he shared how Fero Labs evolved from an academic research project to helping factories save millions in waste while reducing their environmental impact.

From Academia to Industry The transition from Columbia’s electrical engineering PhD program to founding a company wasn’t planned. “One of my main intention, in fact, with going to the PhD was to be a professor, but also to do research and essentially be an entrepreneurial professor of sorts,” Berk explained. But as his research progressed, he saw bigger opportunities beyond academia.

“Throughout my PhD, I decided that maybe the pure academic route was not exactly for me,” he shared. After a few early startup attempts (including the obligatory “restaurant app”), Berk and his co-founder Alp saw an opportunity to apply their technical expertise where it could have massive impact.

Finding the Right Problem “For both of us, our main motivation was to take what were really good at and apply it to real problems that will have impact,” Berk noted. The industrial sector stood out. “When you think of things from the perspective of a factory that making steel or that’s making chemicals… in 1 second, this is more emissions than all the changes that I could be making over the course of a year.”

While sustainability wasn’t yet mainstream in 2015, they saw how AI could simultaneously reduce waste and improve profitability. “Our goal all along was to reduce waste, reduce industrial waste using AI, using our specific kind of AI, white box AI,” Berk explained. “Initially our pitch to our customers was, well, using this, you’ll waste less raw materials, you’ll waste less processing time, and as a result you’ll become more profitable.”

Building Trust in a Conservative Industry The challenge wasn’t just technical – it was earning trust from industrial companies to influence their critical operations. Their breakthrough came through a combination of humility and transparency. “Being very upfront, being very humble since day one, very clear that we don’t know anything about steel,” Berk shared. “Our value add here is not steel production. What we bring in is the data science part, and you guys are the experts.”

This approach resonated with their first major customer, who used their AI recommendations to save a million pounds of raw materials in just one year. More importantly, it validated their vision of making AI accessible and trustworthy for industrial users who weren’t data scientists.

The Future: An Industrial Intelligence Layer Looking ahead, Berk sees AI becoming the central nervous system for industrial manufacturing: “The industrial sector is very much like an organism, where you have factories that produce one thing in one side and then send it to the other. And I really believe that we can go from optimizing a single factory to optimizing the entire organism using AI.”

With a growing team of 25 people and increasing pressure on manufacturers to improve both profitability and sustainability, Fero Labs is positioned to play a crucial role in this transformation. “I believe that in 5-10 years, AI machine learning will be a core part of every factory in the world,” Berk predicts. “And of course, our goal is to make sure that Fero plays a key role in this future.”

For technical founders, Fero’s journey shows how deep expertise combined with genuine humility can open doors in even the most conservative industries. Sometimes the biggest impact comes not from disrupting an industry, but from helping it evolve by solving real problems in ways that empower rather than replace human expertise.

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