Build an AI-Powered Customer Onboarding System

Independent insurance agencies rely on an Agency Management System (AMS) like Applied Epic or Vertafore as their system of record. These platforms are excellent for managing policies and client data but are not built for intelligent document intake. The onboarding process remains a significant source of manual, error-prone work for your staff.

Consider onboarding a new small business client with 20 employees. You receive 20 separate PDF enrollment forms, some scanned, some filled out digitally, and some handwritten. An account manager must manually open each PDF, find the key information (name, DOB, plan choice, dependent details), and re-type it into the AMS. This can take 10 minutes per form, totaling over 3 hours of work for one new client, just on data entry. Any missing signature or illegible field requires a follow-up email, delaying the process further.

This manual workflow creates two core problems. First, it is slow and expensive, consuming valuable staff time that could be spent on client service or sales. Second, every instance of manual data entry introduces the risk of errors, which can lead to incorrect coverage, compliance issues, and client dissatisfaction. Off-the-shelf document readers fail because they cannot handle the variation in insurance forms or apply the specific validation rules your agency needs, such as confirming dependent DOBs make sense relative to the primary insured.

The structural issue is that your AMS is a database with a user interface, not a data processing engine. It expects clean, structured data. There is no feature within Applied Epic or Vertafore to read a scanned document, extract the required fields, check for completeness, and create a new client record automatically. This gap forces your skilled staff to act as human optical character recognition (OCR) engines, a low-value task that creates a bottleneck to growth.

The engagement would begin with a discovery phase to audit your current onboarding workflow. Syntora would analyze a sample of 20-30 recent enrollment forms across your primary carriers to map all required data fields. We would also evaluate the integration points for your specific AMS to determine the best method for pushing validated data into the system, whether through a formal API or another secure connection method.

Based on that audit, the technical approach would involve a serverless pipeline on AWS Lambda. When a new enrollment form is received, it triggers a Lambda function that sends the document to the Claude API for data extraction. Claude's large context window is ideal for parsing multi-page insurance documents. The extracted data (a JSON object) is then validated by a FastAPI service using Pydantic models to enforce business rules, such as checking for required signatures or valid date formats. We have used this exact pattern to process complex financial documents, and it applies directly to insurance forms.

The final deliverable is a system that fits directly into your existing process. Your team would upload new client forms to a designated folder. The AI pipeline processes them automatically, and within 60 seconds, the client data appears in your AMS. Any forms with missing information or data that fails validation are flagged in a simple dashboard for human review, with the specific issue highlighted. You receive the full source code and a runbook for maintenance.