Problem & why
The bottleneck is not intelligence.
It is choosing what to test.
Teams already read papers and run models. What they lack is a ranked, explainable shortlist of the next highest-value experiment directions — scored on evidence strength, novelty relative to the goal, and practical risk — before capital and calendar are locked.
Wrong experiments cost months
In materials, batteries, catalysts, and specialty chemistry, every cycle burns time and budget. Open-ended literature review and model runs do not produce a decision-ready board. Leadership needs a clear go / no-go shortlist — not another pile of papers.
Why Sirmint
Sirmint is the AI Scientific Decision Layer built specifically for Materials & Energy R&D. It ranks experiment directions by Evidence, Novelty, and Risk so founders, CTOs, and R&D leads can protect runway and raise the quality of every technical bet before capital is committed.
Private by design
Research content you share is processed only to produce ranking outputs for your team. It is not used to train public models. Your data stays yours — essential where IP is the product.