AI Automation for Insurance Policy Management

Most agencies rely on the rating tools built into their AMS, such as Vertafore PL Rating or Applied Rater. These are effective for standard personal lines but are too rigid for complex commercial policies or specialty carriers. Their data models are fixed. If you need to compare a unique endorsement for a construction client, there is no field for it. This forces account managers out of the AMS and into a manual process.

For example, consider an account manager preparing a renewal for a commercial client. They need quotes from three standard carriers and two specialty carriers. The standard quotes come from the AMS rater, but the specialty quotes require logging into two separate portals, each with a different layout. The manager manually re-keys dozens of ACORD fields and then copy-pastes coverage limits and premiums into a spreadsheet. This task takes 45 minutes per client and is riddled with potential for data entry errors that create E&O risk.

The structural problem is that off-the-shelf software is built for the 80% case. The architecture of these platforms prioritizes broad compatibility over deep integration with the specific carriers that give your agency its edge. They are closed systems that prevent you from building the custom connections you need. This forces your most experienced people to spend their time on low-value data entry instead of advising clients.

The first step would be a deep dive into your agency's policy management workflow. Syntora would map every carrier you work with, audit their portals to assess automation feasibility, and analyze the structure of your policy documents. We would need to understand how your team currently uses your AMS, whether it is HawkSoft, Applied Epic, or Vertafore, to identify the exact integration points for the new system.

The system would be built as a set of AWS Lambda functions controlled by a FastAPI service. For carriers with modern APIs, we would build direct integrations. For older portals without APIs, the system would use secure browser automation with advanced error handling to navigate and extract data. The Claude API would parse unstructured data from PDF policy documents, extracting specific coverage limits and endorsements. All normalized data would be stored in a Supabase database.

The final deliverable is an API that plugs into your existing workflow. An account manager could trigger a policy comparison from a simple dashboard. The system runs in the background, gathers all the data, and returns a formatted comparison document in under 2 minutes. You receive the full source code, a runbook for maintenance, and the system runs in your own AWS account for less than $50 per month in hosting costs.