Most teams that try AI on documents get an impressive demo and an uncomfortable pilot. This is how we build one that holds up in front of real reviewers. It describes our method, not a specific client.
Agree what “right” means first
Before any model work, we sit with the reviewers and build an evaluation set: real documents, the fields that matter, and the answers an expert would give. Every later decision is measured against it.
Extract with evidence
- Every extracted value carries the page and the passage it came from.
- Low-confidence answers are flagged instead of guessed.
- Documents the system hasn’t seen before go to a person, not to the model.
A person approves the decision
The assistant prepares the work; a reviewer accepts, corrects or rejects it. Corrections feed the evaluation set, so the system gets measurably better instead of quietly drifting.
Launch with guardrails
- Run it in the background on live documents and compare its answers with the reviewers’.
- Turn it on for one document type and one team.
- Widen it only when the evaluation results hold.
What stays in your control
The documents stay in your cloud account, the model can be swapped without rebuilding the workflow, and the evaluation set is yours to keep.
This is an engagement blueprint: how we run this kind of project. It describes our method, not a specific client. Want to see how it would apply to yours? Talk to us.