Build an AI Assistant for Your Insurance Agency's Policy Management

Most small agencies rely on their AMS, like Applied Epic or Vertafore AMS360, for communication. These platforms have template-based automation, but it is rigid. The system can send a pre-written email 90 days before a policy expires, but it cannot understand the client's reply asking to add a new vehicle or change their coverage.

Consider a 10-person agency managing 3,000 policies. A Customer Service Representative (CSR) spends two hours every morning running reports to identify upcoming renewals. For each one, they manually find the correct carrier application, pre-fill it, draft a personalized email, and set a calendar reminder to follow up. When a client replies with an unstructured request, like "Can you add my son's new car to our policy?", the entire automated workflow breaks. That email gets flagged, and the CSR must manually parse the information, find the policy, and create a task in the AMS. This manual work is slow and a major source of E&O risk from data entry errors.

The structural problem is that your AMS holds structured data (policy numbers, dates, coverage amounts) while your inbox holds unstructured data (emails from clients). Off-the-shelf tools cannot bridge this gap. They operate on fixed database fields, not the nuances of human language. They cannot read a PDF of a competitor's quote and create a comparison or understand an email to triage it to the right person on your team. This leaves your staff to act as the expensive, error-prone bridge between the two systems.

Our engagement would begin with a 3-day audit of your current communication workflows. We would map the exact process for renewals, policy endorsements, and client inquiries, focusing on how data moves between your inbox and your AMS. The deliverable from this phase is a precise workflow diagram and integration plan, so you know exactly what would be automated before any code is written.

The technical approach would center on a Python service running on AWS Lambda. This service would use the Claude API to read, classify, and extract data from inbound emails. For a policy change request, the Claude API would identify the policyholder, the type of change, and the relevant details (e.g., vehicle VIN). This structured data would then be used to create a pre-filled task in your AMS via its API. We use FastAPI to expose a simple internal dashboard for monitoring and Supabase for a permanent audit log of every automated action. The entire cloud infrastructure would typically cost less than $50 per month.

The delivered system would monitor a dedicated inbox, like service@youragency.com. It would handle the 90, 60, and 30-day renewal reminders, including collecting documents from clients and notifying your team only when manual intervention is needed. For routine requests, it would provide instant, audited entry into your existing AMS workflow. Your CSRs would shift from data entry clerks to exception handlers, focusing only on the tasks that require their expertise.