Clinical LLM chat interface backed by RAG retrieval, including source ingestion, embeddings, and retrieval pipelines across 1M+ clinical sources. Now used 500K+ times by 30K+ prescribers.
Problem
Prescribers needed fast answers from trusted sources while actively working.
Relevant info spanned too many sources for manual lookup.
A generic chatbot wasn't enough — answers had to be grounded, sourced, and reliable at scale.
Solution
Clinical RAG chat with source ingestion, embeddings, and vector search across 1M+ sources.
Answers grounded in retrieved reference content, not model memory.
Natural-language Q&A for prescribers, tied to authoritative source material.
Conversation Actions
The chat flow also supported workflow-specific actions around the clinical answer: prior authorization templating, insurance checks, and pharma contact forms for specific drugs.