Sheaf Theory · the H¹ Inconsistency Index PATENT PENDING

A numerical measure
of where your models disagree

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 →

Where will the S&P 500 be 12 months from now, and what's the biggest risk to that call?

the desk · Fundamental · Macro · Quant · Technical · Risk Run consistency check →

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.