A Gulf judicial body
Government / Judiciary · Gulf
The shape of this problem
The answer exists and nobody can find it
The problem
Teams needed structured data from scanned material without compromising confidentiality. Judicial material is the case where the usual document-AI answer - send it to a hosted extraction service - is simply unavailable, so the real choice had been between manual processing and no processing at all. That constraint is not a preference to be traded off against accuracy; it determines the architecture before any accuracy question can even be asked.
What we built
We built a private vision workflow combining OCR, layout-aware extraction, and itemisation, with all processing kept inside a controlled environment.
What moved
| Measure | Before | After |
|---|---|---|
| Handling time per document | 21 minutes | ~3 minutes |
| Backlog processable | not measured | all of it, where previously none was - no permissible route existed |
| Documents, images or extracted values leaving the controlled environment | not measured | none |
Handling time per document
Figures are drawn from the practice's own delivery records for the engagement named, measured against the process that preceded it. They have not been through third-party audit, and none is presented as an average across clients.
What it turned on
The deployment boundary was the design input rather than a detail settled afterwards, which is what made the capability available to material that could never have left the environment. Relevant to courts, regulators, and anyone whose most valuable documents are the ones they cannot send anywhere.
- Service line
- Semantic Search
Start here
Which of the four is yours?
Tell us the documents and the monthly volume and we will send the two closest records, with the proof behind each and the honest note where the match is partial.
You get a reply within one working day, from the engineer who would do the work - not a sales sequence.