Documents, understood.
Judgment, preserved.

01 / Decision record

The reasoning
underneath the result.

01The constraint

Manual review consumed thousands of person-hours each month. Keyword automation found only a fraction of the relevant content and generated too many false positives.

02The decision

Combine semantic retrieval with LLM extraction and classification. Route uncertain results to a human, and track accuracy, recall, and drift through a dedicated evaluation framework.

03The trade-off

Automation is not the objective at every confidence level. Keeping a review path makes uncertainty an explicit part of the system.

04The outcome

Manual review fell by 90%, with 97% extraction accuracy reported across a platform processing more than ten thousand documents a day.

02 / System boundaries

Every part has
a clear responsibility.

A high-level reading of the published architecture. Boundaries shown here are conceptual, not a deployment diagram.

03 / Outcomes

What changed.

Results reported in this case study. These are separate from the illustrative routing model.