Build AI Agents That Pay for Themselves in Months

Many businesses start by setting up email rules or using simple parsing tools. For a regional insurance agency with 6 adjusters, this means manually reading new claim emails, copying details into a claims system, and categorizing the submission. This process is slow, error-prone, and consumes hours of an adjuster's day that could be spent on higher-value work.

Dedicated email parsing services can extract data from templated emails, but they break down with real-world complexity. They cannot read a 10-page police report attached as a PDF, understand the unstructured narrative of an incident, and make a judgment call on urgency. They are point solutions for data extraction, not for orchestrating a multi-step business process.

Connecting multiple tools to patch this together creates another failure point. An automation that tries to read an email, send a PDF to an OCR service, wait for the result, and then update a database often times out or loses state if one step fails. Without proper state management and error handling, these brittle workflows require constant manual supervision, defeating the purpose of automation.

Syntora's approach would start with a discovery phase to understand your specific workflow. We would begin by auditing your current process, connecting directly to the source system, such as an M365 inbox, using Python and the `imap-tools` library. For attachments, `PyMuPDF` would be used to extract text from PDFs and invoices. Syntora would analyze historical data, perhaps 2,500 claims over three months, to define the key data points your team extracts manually. This discovery phase delivers a precise JSON schema that guides the agent's output.

The core of the system would be a supervisor agent, implemented in Python using a LangGraph state machine, to orchestrate the workflow. When new data arrives, it would trigger specialized sub-agents. A `Document-Parser-Agent` would use the Claude 3 Sonnet API to read relevant text and attachments. An `Entity-Extraction-Agent` would then pull out specific details like policy numbers, incident dates, and claimant names. Finally, a `Categorization-Agent` would assign a priority and claim type based on your established business rules.

The multi-agent system would be packaged in a Docker container and deployed as a serverless function on AWS Lambda. An AWS EventBridge rule would trigger the agent regularly to process new inputs. The structured JSON output would then be integrated via webhook into your internal management system. This system is designed to significantly reduce manual processing time per item.

A crucial component would be a human-in-the-loop escalation path. If the agent's confidence score for a categorization is below a defined threshold, for instance 85%, it would post a summary to a designated Slack channel for human review. We would incorporate `structlog` for structured logging, feeding data into a Supabase database. This setup would provide a complete audit trail and ensure transparent operations. The typical cloud infrastructure cost for this architecture is under $50 per month.