Build an AI Policy Recommendation Engine for Your Agency

Independent agencies rely on their AMS as a central source of truth. Platforms like Vertafore and Applied Epic are excellent for client management and record-keeping, but they are not built for dynamic, multi-carrier data extraction. When it is time to find the right policy, agents are forced into a fragmented, manual workflow that creates significant inefficiency and risk.

Consider an agency with 15 employees quoting a new general liability policy. The agent logs into the portals for Chubb, The Hartford, and two specialty carriers. They re-key dozens of fields from an ACORD form into each portal, wait for the quotes, then download four different PDF documents. The final step is manually building a spreadsheet to show the client a side-by-side comparison, a 45-minute process where a single copy-paste error could misrepresent coverage and create E&O exposure.

The structural problem is that an AMS is a database, not an integration engine. Comparative raters attempt to solve this but often fall short for commercial lines, lacking support for specialty carriers or complex endorsements. They present a fixed data model. If a new coverage type becomes critical for your clients, you cannot simply add it. This forces your most experienced agents to spend their time on low-value data entry instead of high-value client advice.

Our process would begin with a technical audit of the 5-10 carriers that represent 80% of your business. We would map out how each carrier portal exposes policy data, identifying which have APIs and which will require browser automation. This discovery phase produces a clear data-gathering strategy and a plan for integrating with your specific AMS platform.

The core of the system would be a FastAPI service that orchestrates the data collection. When an agent requests a comparison, this service would trigger parallel AWS Lambda functions, one for each carrier. This parallel architecture is key; it means running 5 carrier comparisons takes roughly the same time as running one. Each Lambda function would retrieve the policy data, then pass the raw output (HTML or PDF) to the Claude API. The Claude API parses the unstructured data into a standardized JSON schema, a pattern we have successfully used for complex financial document analysis.

The delivered system provides a simple interface for your agents to initiate comparisons and view the results. The normalized data is stored in a Supabase database, creating a historical record you own. The final comparison can be pushed back into a custom field in your AMS or viewed on a standalone dashboard. This system would reduce a 45-minute manual task to a supervised, 90-second automated process.