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Argon AI is a platform where biopharma and life sciences professionals can execute complex and data driven workflows using natural language. We help professionals get thorough answers to questions about clinical trials, existing treatments, healthcare landscape, and the competitive market in minutes rather than months. Prior to starting Argon, Samy was responsible for Flatiron Health’s (Roche) first data analytics project which eventually led to the start of the Flatiron Services business unit. There, he saw first-hand the heavily manual process that biopharma companies struggle through to get the insights they need to drive forward their drug development programs. He also helped close over $6M+ in ARR and is an expert in enterprise pharma sales. Cyrus previously led engineering teams and built 0 to 1 across pre-seed and unicorn startups, managed mission-critical trade generation systems at Bridgewater, and held AI advisory roles at various startups. Cyrus has a duel degree in EE and CS from USC. Breakthroughs in AI present an opportunity to reinvent biopharma and life science workflows to reduce the time and cost of bringing treatments to patients where delays in bringing a drug to market can cost a pharma company $3M / day.

Yoneda Labs provides software to help chemists optimise reactions. When chemists make a drug or a material, we help them figure out the best reaction parameters such as temperature, concentration and catalyst. When Jan was working at chemical labs, he experienced the struggle of spending weeks guessing reaction conditions. We then started experimenting with ML to speed up the process. Now, as a team of three friends from the University of Cambridge, we’ve spent the last month combining our domain expertise in Computer Science, Machine Learning and Chemistry to develop state of the art models for reaction optimisation. Although ML is becoming well established in other fields, current chemical models generalise poorly and require lots of programming experience. We make our models easily accessible to chemists in the lab. Finding the right conditions quickly allows pharmaceutical companies to test more drugs, and finding better optima makes manufacturing process cheaper and more environmentally friendly.