Cohort: 3

Members: (Valterra Platinum) Steven Bolleurs, Mark Freed, (Dunia Innovation) Ahmed Ismail, Rishi Kumar, Vineeth Nair

Problem: Industrial catalyst and materials R&D remains slow, fragmented, and heavily dependent on manual experimentation, with limited comparable evidence across materials, conditions, and performance metrics. This is especially critical in emission control and industrial systems, where material performance directly affects cost, uptime, and large-scale value creation, with development cycles and cost exposure reaching up to USD 300M.

Approach: a closed-loop autonomous platform that combines autonomous robotics, AI experiment selection, simulations, and real-time learning to accelerate discovery, delivering experimental capacity in one day that is equivalent to the weekly output of 15 scientists. 

Result: Increased industrial experimentation throughput up to by up to 5,500%, approximately 56x, reduced R&D timelines from months to weeks, and lowered operating costs from up to USD 2,000 to less than USD 300 per experiment.