The Transcendentals
Twenty-four centuries ago, Aristotle identified three irreducible properties of being - the Good, the True, and the Beautiful. Of these, it is the True that bears most directly on the crisis of our moment: the claim that what is, is - that reality has a structure independent of desire or convenience, and that the mind's highest function is not to create but to correspond.
We have built machines of extraordinary fluency - systems that generate language with a confidence that mimics understanding. But fluency is not fidelity. Confidence is not correspondence. Modern AI predicts what is plausible, not what is true. It optimizes for the next word, not the right one. And so it hallucinates - not as a defect, but as a natural consequence of its design. It was never built to touch truth. It was built to approximate it convincingly enough that the difference would not matter.
In law, in medicine, in governance - where a single misattribution can shatter a precedent or a life - the difference is everything.
Omniarch exists because we believe the ancient demand - to know what is, and to prove that you know it - is not a relic of a slower world, but the most urgent engineering problem of ours.
What We've Built
Most AI systems work in a single pass - question in, answer out, no memory of how the answer was formed. The confidence of the output has no relationship to the soundness of the reasoning. Omniarch is built differently.
Omniarch is a proof layer that sits beneath any system making claims about the world - and ensures those claims are sourced, verified, and auditable before they reach a human decision. Rather than relying on a single model's confidence, we decompose unstructured knowledge into a network of independent, domain-specialized knowledge graphs - each one authoritative in its own field, each one evaluating claims without knowledge of the others. Every claim is traced to a named source. Every conclusion carries its full chain of evidence. Where independent graphs converge, confidence is established. Where they diverge, the divergence is surfaced - not averaged away.
What makes this provenance real, not cosmetic: the chain of evidence is not generated after the fact. It is the output. Every step in the reasoning - which source, which authority, which version, whether independent graphs agree - is computed as part of producing the answer, not appended to dress it up. The proof is not a feature of the system. It is what the system produces.
The output is not a summary. It is a proof.
We are starting in law - where the cost of error is highest, the standard of proof is most explicit, and the need is most urgent. Our system ingests court opinions, statutes, and regulatory texts, decomposes them into structured authority, and produces what no legal AI tool can today: a complete chain of proof from conclusion back through legal rules to underlying authority. Every holding separated from dicta. Every jurisdiction mapped. Every precedent tracked through time.
Who We Are
Omniarch was founded by Dan Tretola and Gian Scozzaro - two builders who arrived at the same conviction from different directions: that the AI industry's most consequential unsolved problem is not generation but verification.
Dan Tretola
Dan builds systems at scale. He was an early architect of Facebook's monetization infrastructure - conceived and shipped Custom Audiences, led an off-roadmap lab focused on advanced targeting with n-grams and ML, and helped scale the ads business from $100M to $15B. He knows what it takes to build a platform that other systems depend on.
Gian Scozzaro
Gian puts complex systems into regulated hands. He led the Americas in sales at PTC - birthplace of MEDDIC - scaled Bolt 10x during hyper-growth sourcing roughly 30% of company revenue, and has closed over $400M lifetime across F1000 enterprises in legal, fintech, and payments. He knows how to put infrastructure into the hands of people who need it to be right.
We are based in Oakland, CA. We are pre-seed. We are building.
Why Now
The window is open and it is brief. AI adoption is accelerating across every regulated industry. But the infrastructure to verify what these systems produce does not exist. The models are getting faster, more confident, more embedded in decisions that carry real consequences. The proof layer has not been built. Every month without it, the gap between what AI claims and what AI can substantiate grows wider.
We are not building a better model. We are building the layer that makes every model trustworthy.