Closing the lead-to-bind gap with AI document processing
Quotes, endorsements, policies, forms → extract, validate, normalize → AMS360 / Applied Epic / EZLynx / CRM
Six independent agencies ran on a patchwork of AMS and CRM systems, and producers read incoming documents by hand and re-keyed the data into whichever platform each agency used. We built document-processing pipelines that accept any format, extract the fields, validate every one against a strict schema, and map the result into the exact shape each agency's system expects - so thousands of documents a month flow through the pipelines instead of through a producer's hands.
Results at a glance
- Thousands of documents a month processed without manual re-keying
- 6 agencies live across 4 different AMS/CRM systems
- Every field validated against a strict schema before it reaches a policy system
- Producers freed to spend their time on the work that actually binds business
The problem
The client operated six independent agencies (Clifford & Bradford, Bailey Family, PolicyWatch, Sterling, Marathon, and Nau) across a patchwork of AMS and CRM systems - AMS360, Applied Epic, EZLynx, and Dynamics. Producers manually read incoming quotes, endorsements, policies, and forms and re-keyed the data into multiple platforms. Documents arrived in every format imaginable - scanned PDFs, phone photos, hand-filled forms - with layouts that changed from carrier to carrier. That manual step quietly bottlenecked the whole lead-to-bind pipeline and burned out the people you least want burning out: your producers.
What was built
Ingestion and extraction
The pipeline accepts documents in any format - emailed PDFs, scans, phone photos - and extracts the relevant fields using strong prompting with capable models first, plus OCR and cleanup for photographed and hand-written materials.
Validation and schema enforcement
Every output is checked against a strict schema (Zod). If a record doesn't validate, it doesn't pass - failed extractions route to a human review queue instead of corrupting a downstream policy system.
Mapping and routing into the AMS
Validated data is mapped into the exact shape each agency's AMS or CRM expects. The system uses clean APIs where they exist; where a system has no programmatic access, browser automation with Playwright drives it like a person would.
Agents and downstream work
AI agents handle the follow-on tasks - routing, notifications, moving a document to the next step - that producers used to do by hand.
Observability and reliability
Every run is logged end to end. We built a gold-standard test set early and regression-tested every change, so nothing fails silently on the way into a policy system.
How the approach worked
The strategy prioritized simplicity: strong prompting and clean API calls before anything custom. The real challenge was never a single model call - it was reliability across six agencies, four management systems, and documents that never look the same twice.
The results
Thousands of documents a month now flow through these pipelines instead of through a producer's hands. Automated validation keeps the data clean, producers redirect their effort toward sales, and each agency keeps getting value from the systems it already invested in.
Tools & technologies
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