Automate Document Verification for New Policy Applications

Independent insurance agencies run on their Agency Management System (AMS), whether it is Applied Epic, Vertafore, or HawkSoft. These systems are excellent for managing client records, policies, and commissions. However, they are fundamentally databases, not intelligent processing engines. They can store a scanned driver's license, but they cannot read it to confirm the name matches the one on the new policy application.

Consider the workflow for a new personal auto policy. A Customer Service Representative (CSR) receives a PDF packet with an ACORD application, a photo of a driver's license, and the declaration page from a prior policy. The CSR must open all three documents, manually check that the name and address match across all of them, confirm the VIN is correct, and ensure the prior liability limits meet the new carrier's requirements. This takes 15 minutes of focused, error-prone work per application.

The structural problem is that an AMS is built for storing structured data, not for interpreting unstructured documents like images and PDFs. The platform's architecture lacks the AI components needed for Optical Character Recognition (OCR) and Natural Language Processing (NLP). This forces your team into a swivel-chair workflow, copy-pasting data between documents and your AMS, introducing a high risk of errors that can lead to incorrect ratings or E&O exposures.

The result is a bottleneck. Your agency's growth is limited by how many applications your CSRs can manually process, not by how many new clients you can attract. This high-volume, low-value work is a primary driver of burnout and turnover in key agency staff, directly impacting your bottom line.

Syntora would start with a two-day audit of your current document workflow. We would analyze 10-20 sample application packets for your highest-volume policy types to map every field that needs extraction and every business rule for validation. You would receive a detailed scoping document that defines the precise inputs and outputs before any code is written.

The technical approach would use a FastAPI service running on AWS Lambda. When a new document set is received, it would call the Claude API to perform OCR and extract entities like names, addresses, and policy numbers into a structured JSON format. The FastAPI application then applies your specific business logic, for example, confirming the address on the driver's license matches the ACORD form with 99% accuracy. Pydantic models would enforce strict data validation at every step. Supabase would be used for logging each transaction for auditability.

The delivered system would be a simple, secure endpoint that integrates with your existing workflow. For instance, it could be a special email inbox where your team forwards new applications. The system processes the attachments and pushes the validated data directly into the client record in your AMS via its API, flagging any exceptions for human review. This entire process would take less than 60 seconds to complete, and the serverless architecture on AWS Lambda would keep monthly hosting costs below $50 for processing up to 5,000 applications.