Learn how Pydantic AI powered a four-stage agentic RAG pipeline that answers strictly from STCC's triage decision trees, with Pydantic Logfire making every step traceable and auditable, and zero hallucinations across 329 clinician-validated scenarios.
0% hallucinations across 329 clinician-validated triage scenarios
Schmitt-Thompson Clinical Content (STCC), the source of nurse triage guidelines used by most North American medical call centers, partnered with Vstorm to build a four-stage agentic RAG system on Pydantic AI. By treating the triage decision trees as the only source of truth and tracing every step with Pydantic Logfire, the system reached 0% hallucinations across 329 clinician-validated scenarios.
“We approached this as a safety program first.”— Matthew Thompson, Product Manager, STCC, Schmitt-Thompson Clinical Content
Products Used:
Related case studies
View all case studiesQualio, a quality and compliance platform, ships AI features into a regulated industry where customers audit every release. The team uses Pydantic AI as its agent framework and Pydantic Evals to test LLM behavior, gating every deploy behind a pass rate threshold across roughly 160 test cases and 300 evaluations. Because the eval criteria are written in plain language, Qualio's customers' compliance teams can read and approve the same evidence during due-diligence reviews.
General Intelligence Company (GIC) migrated to Logfire, Pydantic’s AI Observability Platform and Pydantic AI to build a live evaluation system for their autonomous agents. The results? Query performance improved 150x, eliminating rate limits and enabling real-time deviation detection and agent self-correction that was impossible before.
Explore Logfire
Explore our open source packages