Claims Processing Automation

This is an illustrative example build, not a delivered client project. It describes a problem we're equipped to solve and how we'd approach it — so you can judge the thinking before you ever commit a dollar.

Insurance claims often arrive as unstructured documents — PDFs, emails, scanned forms — and a person has to read each one, pull out the key fields, check them against policy terms, and decide where the claim goes next. It's slow, repetitive, and the volume only grows.

We map the existing process end to end first, then automate the slow parts:

The goal isn't to remove people — it's to let them spend their time on the exceptions that actually need judgement.

A retrieval-augmented generation (RAG) pipeline grounds the model in your actual policy documents rather than general knowledge. Document parsing handles the varied formats, a validation layer enforces your rules deterministically, and a human-in-the-loop step keeps a person in control of anything uncertain or high-stakes.

A focused build like this typically runs four to eight weeks: discovery and process mapping, then building the pipeline, then validating it against real documents before it handles anything live. You'd see exactly what's automated, what stays manual, and where the guardrails sit before go-live.

Before building, we agree on what success means in numbers — for example, the share of claims processed without human touch, the time saved per claim, and the error rate against a manual baseline. Those targets are defined up front so you can hold the build accountable, not invented after the fact.