Sheaf Information

Enterprises now route decisions through many AI models and agents. When those systems quietly disagree, the contradiction is invisible — and it can ship straight into a decision. Sheaf is the consistency layer for multi-agent AI. Using real mathematics - sheaf cohomology and Satisfiability theory, it measures where independent AI outputs cohere and, crucially, where they contradict each other in ways that averaging or a majority vote can never surface. You get a coherence score, a precise map of where the models diverge, a flag the moment their views can't be reconciled, and a full audit trail. We don't claim to know the right answer. We measure something narrower and provable: whether your models' views can be glued into one consistent whole — and exactly where they can't. Independent and model-agnostic (Claude, ChatGPT, Gemini, Grok, and more), built for the high-stakes, regulated decisions where a confident contradiction is most expensive. Patent pending; method published with a citable DOI. Many minds. One answer.