Automate New Client Onboarding for Your Insurance Agency

Independent agencies rely on their Agency Management System, whether it's Applied Epic, Vertafore, or HawkSoft. These systems are great for managing clients but are rigid databases that demand manual data entry. When a new client signs on, a CSR has to download their prior declaration pages, vehicle schedules, and loss run reports. They then manually transcribe dozens of fields from these PDFs into the AMS. This is 15-30 minutes of low-value work per client.

Generic OCR or document parsing tools fail because they lack insurance context. An off-the-shelf parser can extract text, but it cannot distinguish between a policy effective date and a date of loss, or a primary driver and an excluded one. The output is a jumble of unstructured text that a CSR still has to sift through, defeating the purpose of automation. The risk of a data entry error, like transposing digits in a VIN or coverage limit, is high and can lead to quoting errors.

Consider an agency onboarding a new commercial client with a small fleet. The CSR receives five different PDF documents from the client's prior carrier. They spend twenty minutes typing vehicle details, driver information, and coverage limits into Vertafore. A week later, during a policy review, they discover two VINs were entered incorrectly, forcing them to re-issue documents and apologize to the client. This manual process doesn't just waste time; it introduces unnecessary risk and erodes client trust from day one.

The structural problem is the gap between unstructured client documents and the structured data format your AMS requires. Each carrier designs their declaration pages differently. No off-the-shelf tool can reliably parse this variety. You need a system built to interpret the specific, messy reality of insurance paperwork and translate it into clean, structured data for your specific AMS configuration.

The first step is a document audit. Syntora would start by collecting 5-10 anonymized examples of recent new client onboarding packets. We would analyze each document type you need to process, from ACORD applications to renewal declarations, and map every critical data point to its corresponding field in your AMS. This discovery phase produces a detailed data schema that becomes the blueprint for the entire system.

We would build a data processing pipeline using AWS Lambda and the Claude API. An agent forwards the new client's email to a dedicated address, triggering a Lambda function. The function sends the attached documents to Claude with a precise prompt engineered to extract the fields defined in the data schema. The AI's output is structured JSON, which is then validated for correctness and format by a FastAPI service using Pydantic models. This ensures only clean data proceeds to the next step.

The final system would connect directly to your AMS via its API. After validation, the extracted data automatically creates a new client shell and pre-populates dozens of fields for policies, vehicles, and drivers. The CSR receives an email with a direct link to the new, pre-filled client record in the AMS. A process that once took 20 minutes of tedious typing is completed in under 60 seconds with significantly higher accuracy.