We'll spend a billion to learn something.
We won't lose a euro to a decision we could have gotten right.
Every company runs on decisions. Almost optimal is expensive. OSM returns the provably optimal decision, from the data you already have.
Every company runs on decisions. Almost optimal is expensive.
Every company runs on decisions. Almost optimal is expensive. OSM returns the provably optimal decision, from the data you already have.
Enterprise margin is decided millions of times a day. Pricing, dispatch, scheduling, allocation, computed continuously by software over live data. Agents made these actions cheap to take. But a good action and the best action look identical in the moment, and the gap between them compounds into billions. Acting is solved. Acting optimally is not.
OSM closes that gap. We turn the lakehouse, where the state of the business already lives, into a world model and run a decision engine on top. Mathematical optimization with AI in the loop computes the best action under uncertainty, with proofs of feasibility and optimality, in real time.
And it runs on the stack you already have. No migration. No solver team. No brittle pipeline. The hard optimization research stays our problem. The best action becomes the easiest one to take.
A small team of researchers and engineers, in Germany and the US. We do mathematical optimization research and put it into production. Between us, we've built systems that optimized billions of transactions and saved millions of dollars.
Built by people from
Our team has done this research for decades and continues to publish. Both papers behind the decisionhouse were accepted at VLDB 2026.
VLDB 2026 · Accepted ∎
VLDB 2026 · Accepted ∎
arXiv 2026 · Preprint ∎