Automate New Policyholder Onboarding with a Custom AI Agent

Small agencies live in their AMS, like Vertafore AMS360 or Applied Epic. These platforms are excellent for client records but their onboarding workflows are rigid. They expect structured data entry and lack intelligent document validation, so a CSR has to manually open every PDF a new client sends to check if it is the right form, if it is signed, and if all fields are complete.

Consider a 10-person agency onboarding a new commercial auto policyholder. The client emails a folder with their driver list, vehicle schedule, and a prior declaration page. A CSR must download all 3 files, open each one, and manually verify that the VINs on the schedule match the number of vehicles listed. They then re-type the driver names and license numbers into the AMS. This process takes 15-20 minutes per client and is prone to data entry errors that can cause coverage gaps.

The core problem is that AMS platforms are databases with a user interface, not data processing engines. Their architecture is built for storing records, not for reading unstructured documents like PDFs or JPEGs. They have no native capability to perform Optical Character Recognition (OCR) or use a Large Language Model (LLM) to understand a document's content. Any automation requires bolt-on tools or manual effort.

This manual bottleneck slows down the time to bind a policy and creates a poor first impression for the new client. While your team is busy with paperwork, the client is left waiting. The risk of typos when transcribing data from a declaration page into the AMS is high, potentially leading to incorrect coverage or E&O (Errors & Omissions) exposure for the agency.

The first step is an audit of your current onboarding documents for 3-5 of your most common policy types. Syntora would analyze sample driver lists, prior dec pages, and applications to map the required data fields. We would also review the API documentation for your specific AMS (Applied Epic, Vertafore, or HawkSoft) to plan the data integration. You receive a detailed mapping document before any code is written.

The system would be a FastAPI service running on AWS Lambda, triggered when a new client document arrives. The Claude API would read the document, extract key entities like names, dates, and policy numbers, and validate that all required fields are present. Pydantic models would enforce data structure before writing to your AMS. We use Supabase as a control database to log every document processed and its status, providing a full audit trail. This architecture costs less than $50 per month to run for an agency processing 400 documents monthly.

The delivered agent would monitor a specific email inbox or folder for new client documents. It processes a typical 5-page document in under 30 seconds. If information is missing or unclear, it flags the submission for human review in a simple dashboard. Correctly processed data is automatically pushed to the appropriate client record in your AMS. You receive the complete Python source code and a runbook detailing how to manage the system.