Five real results, not five taglines.
Every number below is on the page it links to, measured the same way it's described here.
We simulated our own fraud, and it caught it.
A malicious agent deleted the test proving the bug. The first version of Verify said PROVED. After the fix, the same case says CHEATED.
See how it verifies →Every coefficient exact, on the field's own benchmark.
14 dynamical systems from SRBench's Strogatz set, recovered with exact coefficients — up from 5/14 before a compositional rewrite of the engine.
Read the proof dossier →Zero forgetting, proven by formula.
Teach a model something new and it usually forgets what it knew. DAS measures absolute zero deviation in prior knowledge — guaranteed by construction, not optimized toward.
See the method →Zero wasted experts, proven at scale.
Traditional mixture-of-experts training leaves 20%–56% of experts dead weight, never recovered. ACE measures zero wasted experts, at every scale tested — integrated with DAS.
See the method →One architecture, six pieces — most of it already proven.
DAS, ACE, Discovery and hallucination reduction are measured and shipped. Self-training and durable weight memory are next — we don't publish a number until we've proven it the same way.
See MH-AI →About us.
Who's building this, and why.
MOUHN is founder-led and built in France. It started from one specific frustration: a language model that answers a factual question with confident prose is not wrong often enough to be safe, and not honest enough to be trusted in a regulated, high-stakes setting — a reactor manual, a defense procedure, a lab result. Verify, Discovery, DAS and ACE above are the answer to that frustration, built and measured in public.
The company is small and pre-revenue by design at this stage: every capability claimed on this site has a reproducible test behind it, not a slide. That is also the pitch — an engine that would rather say "I don't know" than guess, built by a team that would rather ship four proven pieces than announce ten.
Investors.
France is committing to sovereign AI at national scale — and the sectors that matter most (nuclear, defense, aerospace, healthcare) cannot deploy a system that hallucinates with confidence. That is the gap MOUHN is built for: not a bigger model, a trust layer.
The moat is the refusal. Any LLM can sound confident. Very few systems are architected so the model is structurally barred from answering a factual question from memory — DAS, ACE and Verify above are that constraint, measured, not promised.
Sovereign by construction. On-premise, zero telemetry, data never leaves the customer's infrastructure — a native fit for SecNumCloud-class requirements, not a feature bolted on afterward.
Built to be audited, not taken on faith. Every claim on this site is reproducible by a stranger with no access to us. We apply the same standard to the business: show the evidence, then ask for capital.
Early-stage, France-based, currently financed through non-dilutive deep-tech channels (INPI patent filing, Bpifrance i-Lab). If you fund sovereign, verifiable AI infrastructure in regulated sectors, we want to talk before the pitch deck exists — the engine is the deck.