Automate Underwriting for Faster Policy Decisions

Small agencies rely on their Agency Management System (AMS) like Applied Epic or HawkSoft. These systems are great for client records but offer limited underwriting automation. Their built-in workflows can trigger reminders, but they cannot read a 50-page PDF bundle containing an ACORD 125, supplemental forms, and loss run reports to identify critical risk factors.

Consider a 15-person office trying to quote a new commercial property policy. A producer emails a bundle of PDFs to the underwriter. The underwriter must open each file, find prior carrier information, check for gaps in coverage on the loss runs, and verify the property valuation from a supplemental questionnaire. This manual check takes 20-30 minutes per application, and that is before any real risk assessment begins.

The core problem is that AMS platforms are systems of record, not systems of intelligence. They are built around structured data entry, not unstructured document processing. They lack the native ability to parse PDFs or apply complex, multi-variable logic that mimics a human underwriter's judgment, forcing small agencies into a cycle of hiring more staff to handle volume.

The engagement would begin by auditing your current underwriting process for 2-3 key policy types. Syntora would review your application bundles, document your specific underwriting rules, and identify the exact data points needed for a first-pass review. You would receive a mapping document showing how the AI would extract each required field from your forms.

We would build a pipeline using the Claude API for its advanced Optical Character Recognition (OCR) and data extraction on submitted PDFs. A FastAPI service would orchestrate the process: receiving new applications, passing documents to Claude, and parsing the structured JSON output. This extracted data would then be checked against your underwriting rules, with results written back to a custom object in your AMS via its API.

The final system would run on AWS Lambda for cost-effective, serverless operation, typically under $50 per month. Your underwriters would see a new status on applications in your AMS like "AI Review: Passed" or "AI Review: Flagged," along with a summary of extracted data. This approach has a projected build timeline of 4-6 weeks and allows your team to focus on complex applications, not routine data verification.