The broker’s underwriters were not slow. They were buried. Every commercial submission arrived as a PDF — sometimes a scan of a scan — and someone had to read it, key the fields into three systems, and only then start actually pricing the risk. On a good day that was an hour of typing before any judgment happened. On a bad day, submissions aged out and business walked.
The problem, precisely
The temptation with a problem like this is to reach for a demo-ready chatbot and call it transformation. That would have failed here, loudly. Underwriting is a regulated, high-consequence workflow: a confidently wrong field isn’t a cute hallucination, it’s a mispriced policy. The real constraint was never “can a model read a PDF” — it was “can we trust it enough to remove a human, and where can’t we?”
The approach
So we didn’t try to automate the judgment. We automated the typing, and made the judgment faster and better-informed. Extraction runs on every submission, but every field carries a confidence score, and anything below threshold routes into a review queue instead of silently flowing downstream.
The goal was never a system with no humans. It was a system that spends human attention only where it changes the answer.
What we built
Five stages, deliberately boring, each independently testable:
# pipeline
intake → normalize PDF, split pages, OCR fallback
extract → field model + schema validation
score → per-field confidence, threshold gate
review → human queue for low-confidence only
bind → push to rating engine + audit log
How we knew it worked
Every claim above is backed by an eval suite that ran before a single underwriter touched it: a labelled set of 400 real (scrubbed) submissions, scored on field-level accuracy and, more importantly, on calibration — did low confidence actually correlate with being wrong? It did, which is the only reason the threshold gate is safe to trust.
What I’d do differently
We over-invested in the extraction model early and under-invested in the review UI. The queue is where the humans actually live, and a week spent there in month one would have paid for itself twice. It’s all in the book — including the parts that didn’t work.