Multi-agent AI is a consistency problem — and consistency is a branch of mathematics. Sheaf theory is the math of how local pieces glue into one coherent global whole, and how to detect precisely when they don't. Your agents are locally confident and globally blind; Sheaf measures exactly where they cohere — and where they quietly contradict each other.
Patent pending · Read the technical whitepaper →
Independent AI analysts answer your question separately — the local sections. A market question convenes a five-discipline desk (Fundamental, Macro, Quant, Technical, Risk); any other question, a panel of frontier models. We then run a genuine first-cohomology computation: if the answers can't glue into one consistent whole (a cyclic contradiction), that's a true H¹ obstruction; when they can, H¹ is zero and we report the deterministic H⁰ disagreement over the overlap graph. Computed, not a model guessing a score. AI agents modeling analytical frameworks — for research, not investment advice.
The demo above is a live AI run — real AI models answering your own question, an H¹ that depends on what they say (and, in practice, is usually zero). This is the opposite: fixed inputs, no AI. The classic Rock · Paper · Scissors cycle, computed right in your browser, is guaranteed to produce a genuine nonzero H¹ — so you can see exactly what the obstruction looks like when it's certain to be there.
Sheaf