Building AI for Smarter Insurance Underwriting

Most independent agencies run on an Agency Management System (AMS) like Applied Epic, Vertafore, or HawkSoft. These platforms are excellent systems of record for managing policies and clients. Their limitation is that they are built for structured data entry. They cannot interpret the unstructured data in supplemental documents where critical risk information lives.

Consider a 10-person agency quoting a small commercial property policy. The underwriter receives an ACORD 125 form, a 5-year loss history PDF from a prior carrier, and a narrative from the agent. They must open the loss history, manually read through it to identify any claims over $10,000, and check the narrative for red-flag terms like 'vacant' or 'wood stove'. This manual review takes 30 minutes of focused work for a single application and is susceptible to human error. Missing one key detail can lead to a severely mispriced policy.

The structural problem is that an AMS is a database with a user interface, not an intelligence layer. Its architecture is designed to store data in predefined fields, not to analyze the content of attached PDFs or emails. All the complex business logic for risk assessment lives inside an Excel checklist or in the underwriter's head. This creates a bottleneck where agency growth is limited by the number of applications your team can manually process.

The first step would be a process audit. Syntora would map your current underwriting workflow, from initial application to final bind. We would analyze your underwriting guidelines, sample applications, and supplemental documents like loss run reports to define the exact data points and rules that determine risk. This discovery phase ensures the system is built around your specific business logic.

The core of the system would be a FastAPI service using the Claude API for document analysis. When a new application arrives, a webhook triggers an AWS Lambda function. This function passes the application documents to the Claude API with a prompt engineered to extract key risk factors, score severity based on your guidelines, and summarize the findings. We have built similar document processing pipelines for financial services firms, and the pattern of extracting structured data from unstructured text applies directly to insurance documents.

The delivered system would write its analysis directly into a custom field in your AMS. Your underwriters would see a risk score, a summary of red flags, and extracted data points without ever leaving Applied Epic or HawkSoft. You receive the complete source code, a maintenance runbook, and a system deployed in your own AWS account, ensuring you have full control and ownership.