What Sheaf is
Orchestration is table stakes. Measurement is the product.
Plenty of tools — and yes, Sheaf's own engine — will orchestrate a fleet of AI agents. None of the others can tell you the thing that actually decides whether to trust the result: did the agents reason about a consistent picture of the world, or did they silently contradict each other? Sheaf answers that — over the agents you already run, whoever orchestrates them — with a real number, not a vibe.
What we're not
A chatbot. A model. A confidence score a model invented about itself. (We'll orchestrate your agents if you want — but that's not the point.)
What we are
The coherence layer over your agents — running on Sheaf or on your own stack — proving coherence with genuine mathematics, and halting on a contradiction.
Where it came from
Don't trust one AI. Ask several — and measure the disagreement.
The starting instinct was simple: one model's answer is a guess with a confident voice. Ask several strong models the same thing and the agreement tells you something the single answer never could — and so does the disagreement. The hard part isn't asking. It's measuring.
So we built the measurement properly, on the branch of mathematics designed for exactly this — gluing local views into a global one, and detecting when they can't be glued. That's sheaf theory. The obstruction to a consistent global picture is a real, computable quantity: the H¹ inconsistency index. It catches contradictions that pairwise agreement hides.
Why it matters now
The instrument became a trust layer
When AI only answered questions, coherence was nice to have. Now agents act — they pull records, price things, submit forms — and the same measurement becomes essential: you need to stop the action that shouldn't happen, and prove the reasoning behind the ones that did. That's what Sheaf is today: the coherence layer for agentic AI.
What we believe
How we think about the work
- Measurement over assertion. A number you can check beats a claim you have to take on faith. Everything Sheaf outputs is meant to be verifiable.
- Honest limits. We're precise about what Sheaf verifies and how, and we say it plainly — an instrument that overclaims is worse than none.
- Rigor as the moat. The math is real and published, not decoration. It's what lets a risk officer sign the certificate and a regulator accept it.
Leadership
Jack Widman, PhD
Jack founded Sheaf on a conviction and a habit of mind. The conviction: you can't trust an AI's answer on faith — you have to be able to measure whether its reasoning holds together, and that's a mathematical question, not a matter of opinion. The habit: he's drawn to the places where deep, theoretical mathematics turns out to be exactly the tool a practical problem needs. Sheaf is where the two meet — the coherence layer for agentic AI, a measurement instrument built on sheaf theory and cohomology, the kind of topology he's spent his career in. He holds a PhD in mathematics (topology) and has spent many years building software. Today his work is Sheaf; alongside it, he stays active in research as an affiliated researcher in the foundations of computer science at Ben-Gurion University.
Phil Grady
Phil has spent his career doing the hard part of what Sheaf now faces: turning a rigorous, new measure into a market. He's a venture builder who has taken businesses from a blank page to category leadership and exit — most notably Castlight Financial, which he founded and grew into one of the UK's leading affordability fintechs. What he built there is structurally close to Sheaf's task: a new market category, made by combining open-banking data with credit information, and a score the industry didn't have — then the patient work of getting the institutions that gate trust to accept it. Castlight was founded on making a safer financial world; Sheaf extends that philosophy into agentic AI. Today his work is Sheaf; alongside it he advises and operates across a portfolio of growth-stage companies.