Automate Insurance Underwriting with Custom Python Systems

Most small agencies rely on their Agency Management System, like Applied Epic or Vertafore, for workflow. These platforms handle standard ACORD forms but fail with the unstructured data that defines complex risks. They cannot interpret broker-of-record letters, lengthy email chains, or non-standard PDF supplements containing critical risk information.

Consider an underwriter at a 10-person agency reviewing a new commercial property submission. The packet includes a PDF ACORD 125, an 80-page property specification document, and an email from the broker explaining a nuanced loss history. The underwriter spends 45 minutes manually re-keying data from the ACORD form into the AMS and multiple carrier portals. They must then read the entire 80-page document to find fire suppression system details and manually summarize the loss history. This happens 10-15 times a day.

The structural problem is that an AMS is a system of record, not a system of intelligence. Its architecture is built for structured data storage, not for parsing narrative text or making inferential decisions. When a new risk factor becomes critical, you cannot simply add a field; you are bound by the platform's rigid data schema. This forces underwriters into inefficient manual workarounds with spreadsheets and notes, creating a significant operational drag.

The engagement would start with an audit of your current submission intake process. Syntora would review 20-30 historical submission packets, including PDFs and emails, to map all data sources and formats. We would work directly with your underwriters to define the precise business rules for flagging high-risk submissions. You would receive a technical specification detailing the data extraction logic and integration points with your AMS before any code is written.

The technical approach would use a FastAPI service with the Claude API for document parsing. Claude can extract structured data from messy PDFs and emails with over 98% accuracy. The FastAPI service would expose a secure endpoint that receives new submission emails, triggering an AWS Lambda function that processes attachments in under 30 seconds. Pydantic models validate all extracted data against your required fields before writing it to a Supabase database and your AMS, like HawkSoft, via its API. Total processing time per packet would be under 2 minutes.

The delivered system operates in the background. When an underwriter opens a new submission in their AMS, key fields are already populated. A summary of extracted risk factors, such as 'property lacks a sprinkler system' or 'multiple liability claims in last 3 years,' is added as a note. You receive the full Python source code, a runbook for maintenance, and hosting costs are typically under $50 per month.