Build a Claims Processing Pipeline with Maia and Jev AI

See what Maia can build from a short prompt. Given an S3 bucket of unstructured claims data (PDFs, JSON, text and emails), Maia designs a metadata-driven bronze, silver and gold architecture, ingests every format with Textract and S3 load components, and calls Jev AI to evaluate each claim.

The result is a star schema with claim category, liability, fraud risk, severity and a composite priority score across 440 claims. The build took around ninety minutes, including documentation and regression tests. The demo also covers CI/CD promotion to a QA environment, column-level lineage and agentic DataOps root cause analysis.

This video is part of the Building with Maia and Jev series. Read the full write-ups:

• Building a TypeSafe Jev Connector and Shared Pipelines in Maia
• Jev Use Cases: Five Decisions You Shouldn't Be Paying a Frontier Model to Make
• The Prompt Was Three Sentences. The Build Was a Governed Claims Data Product.