FreeV Summer Updates
Applications for our Fall 2018 Batch XI are open. If you are currently enrolled at UC Berkeley and working on a product or service, we encourage you to apply. Applications are due Friday, September 7th at midnight.
Congrats to Alex Kern and Nikhil Srinivasan from Distributed Systems (Batch VI) on their acquisition from Coinbase!
Congrats to Divy from Paladin Drones (Batch VIII) on becoming a 2018 Thiel Fellow!
Read our full article on Fiat Lux Labs here. Below are some highlights:
Fiat Lux Labs - Decontaminating Beer
Beer is a $111.4 billion market in the US alone. The brewing and fermentation process at a large scale is very complicated and has not changed for hundreds of years. While the tech revolution has greatly influenced manufacturing and production globally, it has left the beer industry undisturbed. Large breweries have always been capital intensive and reliant on manual labor. A group of Cal students are hoping to use an IoT device to improve the brewing and fermentation process.
Fiat Lux Labs was founded by four Cal students and was part of Free Ventures’ Batch IX in Fall 2017. They built a sensor technology platform that tracks metabolic flux, contamination, and bio mass in the biochemical processing industry. Examples of industry applications are brewing, pharmaceutical manufacturing, and synthetic biology. Using their platform, companies can increase profit margins by 10–15%.
Q: How did you come up with the idea to decontaminate beer?
Yash: “We were all part of a biochem lab at Cal and had toyed with the idea of making our own beer for fun. I would set up fermentation batches and manually take samples each hour for 48 hour cycles to test for contamination. We quickly realized that contamination was a big problem and began brainstorming solutions. After considering many ideas, we decided building a sensor for a fermentor was the best way to approach the problem. It was a long process, but that’s how we shaped the science into an actual product.”
Q: What is the advantage your tech has over current industry alternatives?
Yash: “Our competitors are mostly providing expensive hardware for biological tests to companies. They have to use the hardware themselves and it gives a binary answer (contaminated or not). Software isn’t really involved. We wanted to abstract the hardware from the user and provide a platform to track much more than just contamination.”
Q: Why is it difficult to build an ML model in the biochem space?
Yash: “The reason it’s hard to develop this model in the biochem space is that we’re applying ML on experimental data, not just data you can buy. Most other tech companies can buy data from other sources but we have to produce it ourselves and analyze it on top of that. We had started at 70–80% accuracy and we’re constantly working to improve it.”
Be sure to read the full article to learn more about Fiat Lux Labs and the challenges they faced as student entrepreneurs!