ACORD extraction absorbs 14% volume increase with no new hires

Submission triage was the firm's bottleneck. CSRs spent ~45 minutes per packet classifying documents (ACORD 125 vs 126? SOV vs supplemental?), keying into Epic, and rolling up to a submission-ready format. Handwritten loss runs were the perennial pain point.

What we built

• Submission package classifier: routes inbound documents to the correct blueprint (system-generated ACORD, hand-corrected ACORD, broker-customized ACORD, loss run, SOV, supplemental, declaration).
• Custom blueprints for each ACORD form (125, 126, 140 + state-specific variants).
• Loss-run extraction tuned specifically for handwritten variants — separate blueprint, separate confidence thresholds.
• Writeback to Applied Epic via REST API.
• Audit trail: original document, blueprint used, every extracted field with confidence, the human reviewer if any, and the final Epic write — all logged to S3 for E&O purposes.

Caveats

Handwritten loss runs are the IDP industry’s perennial Achilles heel. We deliberately quote ~75–85% accuracy for handwritten content, not 99%. State-specific ACORD variants required custom blueprints we hadn’t initially scoped — we absorbed ~25 hours of unplanned work in the first month and adjusted the SOW template for future deals. Broker-customized ACORDs needed per-broker blueprints that we now bill as a per-broker add-on.

Stack. AWS Bedrock Data Automation with custom ACORD blueprints (125, 126, 140) · Amazon Textract for handwritten loss runs · Amazon A2I · Applied Epic REST · Bedrock Knowledge Base over carrier appetite guides.

99%+ field accuracy on system-generated ACORDs. CSR review time dropped from 45 minutes per packet to ~8. The firm absorbed a 14% volume increase in the quarter without adding CSR headcount.