Most AI vendors tell you their model is always right. It isn’t, and any CFO knows it. We build the opposite: AI that shows its work, cites its evidence, and hands the hard calls to a human. Here is what that honestly means.
Extraction is never 100 percent
Reading messy PDFs, scans and spreadsheets is not perfect, and anyone who claims otherwise is selling something.
How we handle it: every extracted value carries a confidence score, and anything uncertain is routed to a human review gate instead of flowing silently into a decision. We start with the document types a customer actually uses, not all of them at once.
You cannot prove an AI’s judgment is correct
For open-ended analysis there is no single right answer to check against.
How we handle it: every finding links back to the exact source it came from, so a person can verify it in seconds. We benchmark against expert-reviewed cases and gate the consequential outputs through a human. Verifiable, not “trust us”. How the audit trail works →
You cannot fully eliminate hallucination
Language models can still invent things, even with good inputs.
How we handle it: the model only reasons over your documents, never its own guesswork. Every claim must cite the evidence behind it, and when the evidence is missing, that becomes an explicit finding, not an invented answer. How grounded AI works →
The backbone that makes it real
All of it sits on an event-sourced record: every finding and every decision is stored with who, what, when and from which source. Multi-tenant isolation keeps each customer’s data strictly separate. The code is source-available and runs in your own environment, and compliance is built into the framework, so your auditors and regulators can see exactly how it works.
Why institutions trust it
Not because we promise the AI is always right, but because everything it does is grounded, cited, verifiable and governed. That is the difference between a demo and a system you can put in front of a regulator.