Case study 01
Suture
An AI-native operations layer for a cardiology practice: the full referral-to-outreach loop, built to be audited.
Python · FastAPI · PostgreSQL · pgvector · LLM extraction · Hybrid RAG
problem
A cardiology practice runs on paper-shaped work. Referrals arrive as faxes and PDFs, prior authorizations mean reading payer policy documents, and patient outreach is manual phone-tag. Automating any of it with LLMs raises the stakes instead of lowering them: a hallucinated field in a referral or a leaked record is a patient-safety and compliance incident, not a bug. The system had to be useful and provably careful at the same time.
approach
Suture handles the full loop of intake, review, prior-auth, and outreach, with a human kept deliberately in it. Documents flow through an LLM extraction pipeline backed by a real eval harness: per-field precision and recall, tracked across every prompt change, so quality regressions get caught before they ship. Prior authorization runs on a 3-stage hybrid-RAG engine (structured rules plus semantic retrieval) over payer policy documents. Extracted data goes to human review before anything acts on it, and outreach goes out multi-channel only after approval. The security posture is fail-closed: tenant isolation enforced at the ORM layer, where a missing tenant context raises and cross-tenant reads return 404, plus field-level PHI encryption and a PHI-safe audit log.
outcome
A working AI platform for a domain where mostly right is not a passing grade. Extraction with measured per-field accuracy, retrieval that cites the payer policy it decided from, and an architecture where the dangerous failure modes are structurally impossible instead of merely unlikely.