Densery

Audit-trail explorer

Pick any field. Ask the question an examiner asks.

Three real document types, processed by Densery. Click any highlighted value and walk the decision all the way back — which page it came from, which model read it, what confidence it carried, which rule judged it, who signed it, and what was written into the system of record. Two fields on this page failed and went to a human. Those are the ones worth clicking.

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Choose a file

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Select a highlighted value
Every amber value in the document on the left was extracted, judged and written back by an agent.

The two red ones did not clear their confidence threshold and stopped for a human.

Click any of them.
WHAT THIS IS DEMONSTRATING

Reading the document is layer one, and it is close to free — open-weight models now do it locally for cents per thousand pages. We are not asking you to be impressed that the value was extracted. We are asking you to notice that six months from now, when someone asks why a file was closed, this record exists without anyone having assembled it.

The failure path

The interesting fields are the ones that did not work.

Any vendor will show you a clean extraction. The question a risk committee actually asks is what happens on the file where the machine was unsure — and whether you can tell the difference between a value the model read correctly and one it guessed.

  • Silent degradation is the dangerous failure.A tool that reads a Korean personal guarantee badly and reports no error forces a human to re-check everything, and the automation saves nothing.
  • A confidence score is only useful if it routes.Below threshold, the file stops, goes to a named person with the source page attached, and waits. It does not proceed with a guess.
  • The override is evidence too.What the machine proposed, what the human changed it to, who they were and when — recorded as part of the same trace, not in a separate ticketing system.
  • The correction has to change the next run.Otherwise accuracy flatlines at whatever generic quality you bought. Click the red fields and follow the last step.

Try it against your own

This is a demonstration. Yours would use your documents.

The three files above are constructed samples, not customer data — nothing on this page came out of a real institution, and the field values are invented. What is real is the structure of the record: the seven steps, the confidence routing, the named approver and the correction loop are what the banking deployment produces on every file it touches.

In a scoping session we run this on twenty of your own files, redacted if you need them to be, and you get the same explorer pointed at your estate. That is the version worth ninety minutes.

THE EXPORT

What an examiner receives

  • The full trace for any file or date range, as a signed export
  • Model and ontology version pinned per decision, so an old file is explained by the rules in force at the time
  • Every human override with the reviewer's identity and timestamp
  • Threshold and policy change history, with who approved each change

The full security and audit posture →

The next step

Ninety minutes, your documents, three numbers.

A scoping session is not a demo. Bring twenty real files, redacted if you need to. We take three numbers off you — annual volume, fully loaded cost per file today, and what happens when the output is wrong — and hand back a one-page value case in your own KPIs.

If the arithmetic says we are not a fit, we will tell you in the room rather than six weeks later.

Book a scoping session Work out the number first
QUALIFY YOURSELF OUT

We are a fit if all three are true

  • More than 250,000 pages a year, or 25,000 claims or files
  • A legal obligation — regulator, board risk committee or parent-company policy — to keep the data in-house
  • An AI or agent pilot that did not reach production

If your data can go anywhere and your documents are already clean and digital, you do not need us. Use a hyperscaler document API and spend the money on something harder.