Automate New Insurance Policy Applications with AI

Most agencies rely on their Agency Management System, such as Applied Epic or Vertafore, for managing client data. While these platforms are excellent databases of record, their intake tools are rigid. They expect structured data, but new client information arrives as unstructured emails, scanned PDF declaration pages, and photos of vehicle registration cards. These systems simply have no built-in function to intelligently parse a low-quality scan or extract policy details from a competitor's dec page.

Consider this common scenario for a 15-person agency. A producer gets an email from a new commercial prospect with an attached PDF of their current general liability policy. An account manager must open the PDF, manually locate and transcribe dozens of fields into the AMS to create a client record, then re-key that same information into three different carrier portals to generate quotes. This process takes 25-30 minutes per prospect and is prone to costly data entry errors. A single typo in a coverage limit or address can lead to an inaccurate quote and a lost client.

The structural problem is that an AMS is designed for data storage, not data interpretation. Its architecture is built on fixed database schemas that require clean, predictable inputs. These platforms cannot natively run a large language model like the Claude API to understand the context and layout of a unique document. The problem is not a missing feature; it is a fundamental mismatch between the structured world of the AMS and the unstructured reality of client documents.

For an agency processing 100 new clients a month, this manual work consumes over 40 hours of staff time. That is one full-time employee's week, every single month, dedicated to low-value data entry. This bottleneck limits growth, frustrates staff, and introduces unnecessary risk from manual errors that could impact E&O coverage.

The engagement would begin with a thorough audit of your current intake workflow. Syntora would map every document type you process, from standard ACORD forms to carrier-specific supplemental questionnaires. We would identify the 50-100 critical data fields needed for each policy type and define the exact API endpoints in your AMS (like Applied Epic or HawkSoft) for creating new client and policy records. This discovery phase produces a detailed data mapping document that serves as the blueprint for the system.

The core of the solution would be a Python service built with FastAPI and deployed on AWS Lambda for efficiency and low cost. When a new document arrives in a designated inbox, the service sends it to the Claude API with a prompt engineered to extract key-value pairs (e.g., 'Named Insured': 'ABC Contracting'). Pydantic models validate the extracted data against your AMS's required format, catching errors before they are ever saved. This entire intake and validation process would complete in under 90 seconds.

The delivered system is a managed, automated pipeline, not just a script. It creates new client records in your AMS with application data pre-filled, ready for an agent's review. Your team works within their familiar AMS interface, but the manual data entry is eliminated. You receive the full source code in your GitHub repository, a runbook for maintenance, and a simple dashboard to monitor processing volumes. For an agency of your size, a project of this scope is typically completed in a 4-6 week timeframe.