Automate Your Insurance Customer Onboarding with Custom AI

Most agencies rely on a mix of standard web forms, PDFs, and e-signature tools for new client intake. A tool like Jotform can collect data, but it arrives as a flat email or PDF attachment. A customer service representative then has to manually re-key dozens of fields from that submission into your AMS. This process is not only slow but also a major source of errors. A single mistyped digit in a VIN or address can cause quoting and binding issues downstream.

Agency Management Systems like Applied Epic or Vertafore offer client portals, but they are often too rigid for an independent agency's needs. You cannot easily customize the onboarding questionnaire to match your unique discovery process for a niche client, such as a restaurant requiring specific liquor liability details. This rigidity forces agents to revert to sending external PDFs, which completely defeats the purpose of the portal and keeps the manual data entry problem alive.

Consider an agency specializing in commercial lines for contractors. A new prospect fills out a 10-page PDF application and emails it back. A CSR then spends 30 minutes transcribing the handwritten responses and vehicle schedules into HawkSoft. One incorrect DOT number leads to an instant quote rejection from a key carrier, delaying the entire sales process and damaging the client's first impression of the agency's efficiency.

The structural problem is the disconnect between unstructured client input (emails, scans, PDFs) and the highly structured data model of an AMS. Off-the-shelf form builders don't understand AMS schemas, and AMS portals lack the flexibility for specialized intake. This gap forces your staff to act as a human API, bridging the two systems with slow, error-prone manual labor.

The first step would be to audit your current onboarding workflow and all data sources. Syntora would map every field from your client intake questionnaires to the corresponding objects and fields in your AMS, whether it's Applied Epic or a different platform. This data mapping process is critical. It identifies the exact transformations required, such as normalizing addresses to USPS standards or reformatting vehicle identification numbers before they are sent to the AMS.

The technical core of the system would be a FastAPI service hosted on AWS Lambda. When a client submits a web form or emails a document, the service triggers a Lambda function. This function uses the Claude API to parse the unstructured text and extract key entities. We would use Pydantic models to enforce strict data validation, ensuring every field conforms to the AMS requirements before any data is written. This architecture creates a reliable, API-driven link between your public-facing forms and your internal system of record.

The delivered system automatically creates a new client record and pre-fills the initial policy application inside your AMS. This would happen in under 60 seconds from the moment a client clicks submit. You receive the complete Python source code in a private GitHub repository, deployment scripts, and a runbook detailing how to manage the system. The process that once consumed 30 minutes of staff time now happens automatically, freeing your CSRs to focus on client relationships, not data entry.