Implement Custom AI for Claims Processing
AI for claims processing is priced as a one-time build fee, not a recurring per-user license. The final cost depends on claim volume and the complexity of your agency management system integration.
Syntora develops AI solutions for insurance claims processing, focusing on automating the extraction and triage of First Notice of Loss documents. Our approach involves building custom pipelines using technologies like Claude API and FastAPI to integrate directly with existing agency management systems. This enables efficient, structured data intake and routing based on client-specific rules.
Scope is determined by the number of First Notice of Loss (FNOL) formats you receive and the specific routing logic your adjusters follow. An engagement can be straightforward if you primarily receive structured data from a few large partners. It becomes more complex if you process FNOLs from dozens of sources in varied formats like emails, PDFs, and web forms. Syntora would work with your team to define the specific data sources and routing rules required for your operations.
What Problem Does This Solve?
Independent agencies often try to automate claims intake using the built-in tools of their Agency Management System (AMS), like Applied Epic or Vertafore. These systems can create tasks based on email subjects but cannot read the email body or its PDF attachment. An adjuster still has to manually open every FNOL, find the policy number, assess the severity, and copy-paste details into the AMS.
A common next step is to use an email parsing tool like Mailparser.io. This works initially by defining templates to find data at specific locations. But this approach is brittle. When a carrier partner updates their FNOL PDF form, even by shifting a field two lines down, the parser breaks silently. Suddenly, new claims are logged with missing policy numbers, and your team doesn't find out until hours later after dozens of claims have piled up.
This template-based method fails because it assumes consistency where there is none. Maintaining dozens of parsing rules for different carriers becomes a constant, reactive task. A system that requires manual updates every time a partner changes a form is not a real automation solution; it is just a different kind of manual work.
How Would Syntora Approach This?
The engagement would begin with a discovery phase to audit your current FNOL intake processes. Syntora would identify common FNOL formats, crucial data fields for extraction, and your specific claims routing logic. We would require access to a representative sample of historical FNOL emails and attachments to inform model development.
Syntora would then design and build a custom processing pipeline. This typically involves connecting to your claims inbox via the Microsoft Graph API and developing a series of prompts for the Claude API. We have built document processing pipelines using Claude API for financial documents, and the same pattern applies to insurance documents. The approach is designed to extract key fields such as claimant name, policy number, and incident date from unstructured text, adapting to varied formatting rather than relying on fixed templates.
The core processing engine would be developed using FastAPI, a high-performance Python framework. Upon receipt of a new FNOL email, a webhook would trigger the service. The email content would be sent to the Claude API, which would return a structured JSON object containing extracted data, a severity score, and a summary for the adjuster. This process is engineered for rapid execution.
For deployment, the service would typically utilize AWS Lambda, offering a pay-per-use cost model for compute resources. Supabase would be implemented for structured logging of every transaction, providing a full audit trail including raw input, Claude API response, and confidence scores. The delivered engine would integrate with your existing Agency Management System (e.g., Applied Epic, Vertafore, HawkSoft) via its native API to create new claim records and assign them based on your predefined routing logic.
To ensure oversight, the system would include a human-in-the-loop checkpoint for high-severity claims. In addition to creating the claim in the AMS, it would be configured to send a priority notification to a senior adjuster's communication channel, such as Slack, including the AI-generated summary and a direct link to the record. This design combines the efficiency of automated intake with necessary expert review for critical events.
Typical build timelines for a system of this complexity, assuming clear scope and client data availability, range from 8-12 weeks for initial deployment, followed by an iteration and refinement period. Deliverables would include the deployed and tested claims processing engine, comprehensive technical documentation, and knowledge transfer sessions for your internal teams.
What Are the Key Benefits?
First Response in 12 Minutes, Not 4 Hours
The system processes an incoming FNOL report, scores it, and routes it to an adjuster in under 30 seconds, cutting your average initial response from hours to minutes.
Pay Once for the Build, Not Per Claim
A single project engagement fee. After launch, hosting on AWS Lambda costs less than $50 per month for thousands of claims, with no per-user or per-claim charges.
You Get the Keys and the Blueprints
We deliver the complete Python source code in your private GitHub repository, along with deployment scripts and a runbook. You own the system outright.
Monitored 24/7, Alerts in Slack
Every AI decision and API call is logged to Supabase. If the Claude API fails or an integration error occurs, you get an immediate, specific alert in a dedicated Slack channel.
Works With Your Existing AMS
Native API and webhook integrations for Applied Epic, Vertafore, and HawkSoft. Your team works within the system they already know, no new software to learn.
What Does the Process Look Like?
System & Data Access (Week 1)
You provide read-only access to your claims inbox and a data export of 500 historical claims from your AMS. We map your current triage and routing rules.
Core AI Engine Build (Weeks 2-3)
We build the FastAPI service and fine-tune the Claude API prompts for data extraction and summarization. You receive a demo of the system processing five of your real-world FNOL examples.
AMS Integration & Testing (Week 4)
We connect the AI engine to your AMS via its API. We process a batch of 50 historical claims to verify data is written correctly. You get a UAT checklist to sign off on.
Go-Live & Monitoring (Weeks 5-8)
The system goes live on a small subset of claims. We monitor performance for 4 weeks, adjusting logic as needed. You receive the final source code, documentation, and system runbook.
Frequently Asked Questions
- How does claim volume or complexity affect the project scope?
- Scope is driven by three factors: the number of distinct FNOL formats to parse, the number of custom routing rules, and the quality of your AMS API. An agency with one primary carrier partner and standard routing rules is a faster build than one with ten partners using different PDF forms.
- What happens if the AI misinterprets a claim or the system goes down?
- Every decision has a confidence score. Low-confidence extractions are flagged for human review. If the Claude API is down, the system retries 3 times before escalating via a Slack alert. The email is held in a queue for manual processing, ensuring nothing is missed.
- How is this different from automation in Applied Epic or Vertafore?
- AMS automation can trigger tasks based on simple rules, like 'if email subject contains X, assign to Y.' It cannot read the content of an email or its attachments. Syntora's system uses a large language model to understand unstructured text, score severity, and summarize the situation.
- How is sensitive claimant information handled?
- The system runs in your own AWS account, giving you full control. Claimant data is only in transit between your email server, the Claude API, and your AMS. We do not store PII long-term. All data is encrypted in transit using TLS 1.2 and at rest in the Supabase logs.
- How accurate is the data extraction and how does it improve?
- On a stable FNOL format, we achieve over 98% accuracy on key fields like policy number and claimant name. The model does not 'learn' on its own, which prevents drift. When you onboard a new partner with a new FNOL format, we update the prompt—a process that takes about two hours.
- What kind of support is offered after the handoff?
- The initial build includes an 8-week post-launch monitoring period. After that, we offer a monthly retainer for ongoing support. This covers prompt adjustments for new FNOL formats, dependency updates, and on-call support for any production issues. Most clients do not require it unless their business processes change frequently.
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