Modernizing Claims Intake for a Global Commercial Insurer with AI-Driven Decisions

THE PROBLEM

Manual Claims Intake Was Slowing Down the Entire Insurance Operation

This global commercial insurer was grappling with a familiar but compounding challenge: thousands of inbound claims emails and attachments each year, each one needing to be opened, read, routed, extracted, and processed by a team of human handlers. 

The manual effort was labor-intensive and created hidden risk, inconsistent triage, and limited auditability across their claims intake workflow.

Despite being one of the most process-driven functions in insurance, claims intake remained stubbornly analog. Time-sensitive referrals risked missing deadlines. Agent routing relied on tribal knowledge. Fraud checks were inconsistent, and none of it could be scaled without more headcount.

The business needed more than automation. It needed a claims intake process that could reason, route, and explain instantly and at scale.

THE SOLUTION

End-To-End AI Automation Purpose-Built for Insurance Claims

Over a two-week proof of concept (PoC), we deployed our full-stack Agentic AI platform, AgentFlow, tailored to the insurer’s top four intake pain points: 

1. Email and attachment routing 
2. Urgent referral detection 
3. AI-driven assignment
4. Orchestrated decision-making

The system was designed to meet strict regulatory and audit standards, with all data processed in a single-tenant SOC 2 Type II-certified environment and deleted within 72 hours.

Key capabilities included:

• Document AI to classify and extract structured data from any attachment type like PDFs, scanned forms, Excel files, handwritten notes, and align those to the insurer’s taxonomy (e.g., police reports, ACORD forms, legal notices).

• Decision AI to automate four core intake decisions: urgency classification, agent routing, approval status, and fraud detection. Each decision was explained, scored for confidence, and fully auditable.

• Report AI to auto-generate structured summaries in PDF and JSON, including supporting evidence and human involvement metrics.

• Conversational AI to enable natural-language queries about claim status, extracted data, or decision rationale.

• Finder — a semantic search layer that indexes all claims, attachments, decisions, and metadata for rapid retrieval and audit preparation.

All modules were orchestrated together, allowing for smooth exception handling and full traceability across the entire claim lifecycle, from email ingestion to report generation.

THE RESULTS

Clear Results, Real-World Simulation

This wasn’t a theoretical showcase. The PoC was tested on real .eml files exported from live workflows, with full metadata and nested attachments intact.

• Claims were processed end-to-end without opening a single email client.
• Each decision was explained clearly, with exception cases escalated and flagged for review.
• Semantic search made audit prep instantaneous, so no digging through inboxes or local folders was necessary.

Most importantly, the platform proved it could be a daily-use tool for real claims teams. Not a technical demo, but a scalable replacement for spreadsheets, folders, and fragmented decisions.

About the customer

This customer is a U.S. division of a global commercial insurance group, handling a wide range of specialty, casualty, and multiline coverages. Their operations demand high-touch service, fast triage, and watertight compliance, all of which depend on getting claims intake right.