A teaching demo of agentic AI: upload a claim, watch the agent extract the data, retrieve the relevant rules, validate them one by one, and explain its decision — with every step of its reasoning visible.
All data is fully synthetic. No real patient information is used anywhere.
Extract. The model reads an unstructured claim document and pulls out structured fields.
Retrieve. It searches the policy rules for the ones most relevant to the claim (RAG).
Validate. It checks the claim against each rule — pass, fail, or unclear — with reasoning.
Decide. It synthesizes a verdict (approve / deny / needs review) and explains why.
Evaluate. Score the whole labeled set against ground truth with a confusion matrix.