A model you cannot trust is just an expensive drawing. Before your team builds analyses, reviews, and decisions on top of a model, you need to know it is logically sound. That is exactly what automated reasoning gives you.

OML Code works on two levels. Entailment derivation runs live and incrementally: as you edit, the reasoner infers the knowledge your model implies, so derived facts are always up to date. Consistency checking runs on demand: it tells you whether the model contains contradictions, and when it does, it shows you exactly which statements lead to the inconsistency.

What this means in practice

• Errors surface while you model, not months later in integration or review
• Inferred knowledge enriches the model automatically, so you assert less and get more
• Inconsistencies come with explanations, so fixing them is fast instead of forensic
• Reviews shift from debating opinions to inspecting verified facts

Interface errors caught late cost 10 to 100 times more to fix. Reasoning moves that detection to the moment the error is introduced.

If you have ever shipped a spec with a contradiction nobody caught, you know why this matters. Ask us to show it on a model like yours.