The Transcendentals
Twenty-four centuries ago, Plato 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 fabricates with the conviction of someone who was there, 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
We have built the verification layer for legal AI. Not a search index. Not a document retrieval system. A structured assertion database: every legal holding, every piece of reasoning, every citation and its treatment history, extracted at the sentence level from federal case law and stored as a machine-readable record with full provenance.
The extraction is pipeline-driven, not generated. Fourteen analytical layers spanning structural segmentation, modality classification, speaker attribution, and normative typing process each sentence and produce a structured record: what was decided, by whom, in what role, with what authority, binding which jurisdictions, and whether subsequent courts have affirmed or narrowed it. The same input produces the same output every time. There are no stochastic components between document and assertion record.
The output is not a summary. It is a proof.
We started in federal case law, where the standard of proof is most explicit, the cost of error is highest, and the need is most urgent. Federal courts since 1951. Every circuit. Every assertion speaker-attributed to majority, concurrence, or dissent. Every precedent tracked through time with three independent temporal clocks: opinion date, statute effective date, applicability window.
Legal AI entered production. Verification infrastructure did not. That is what we built.
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.