We build AI that earns the trust it asks for.
EigentixAI exists because document AI has a trust problem: a model returns an answer, but not the evidence. We build the evidence in — grounding, human review, and an audit trail — so teams can put AI to work on the documents their business actually runs on.
What we hold to
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Trust is shown, not asserted
We don’t ask you to believe an accuracy number. We show you where a value came from, who verified it, and when. Provenance is the product.
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Honest about the edges
Every system has limits. We publish ours — cold-start behavior, manual steps, what isn’t built yet — because a vendor you can’t calibrate is a vendor you can’t trust.
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Deterministic where it counts
The model plans; deterministic tools do the arithmetic. Where a wrong number has consequences, we don’t leave the math to a language model’s guess.
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A human holds the pen
Automation earns scale; people hold judgment. Review is built into the pipeline, not bolted on after — the loop pauses for a person exactly where it should.
Why “Eigentix”
In linear algebra, an eigenvector is the direction that survives a transformation unchanged — the essential structure underneath the noise. That’s the whole job: take a messy stack of documents and recover the structure that matters, precisely and verifiably. The name is the mission.
We’re a focused team building one thing well: a document-extraction platform you can audit. We’d rather ship a narrow capability that’s trustworthy than a broad one that isn’t. Where the industry reaches for a bigger model, we often reach for a local, deterministic path — because milliseconds, zero token cost, and a result you can explain beat a heavier answer you can’t.
See how DocAI works