Corporate — 2026
Document triage that gives a team its week back
An AI extraction and routing layer for high-volume inbound documentation.
Measured
Accuracy against a 12-month evaluation set
Exceptions only
What reaches a human
Full
Audit trail on every decision
Client
Financial services provider
The problem
A specialist team spent most of every day opening documents, classifying them and routing them to the right queue.
The challenge
Accuracy could not be traded for speed. Every automated decision needed to be explainable and reversible by a human.
Discovery
We built an evaluation set from twelve months of historic documents before writing a line of production code, so accuracy was measurable from the start.
The solution
Structured extraction with confidence scoring: high-confidence documents route automatically, everything else lands in a review queue with the model's reasoning attached.
Technology
- LLM orchestration
- Structured extraction
- Evaluation harness
- Review tooling
Outcome
The team moved from processing documents to supervising a system that processes them, handling only genuine exceptions.
- — Routine classification handled without human touch
- — Every automated decision auditable with its evidence
- — Specialists redeployed onto exception and advisory work
Inside the build
Extraction pipeline
Classification with per-field confidence.
Review queue
Human oversight where confidence is low.
Evaluation dashboard
Accuracy tracked continuously, not assumed.
What we learned
- — Build the evaluation set before the model.
- — Trust comes from showing the working, not from the accuracy number.