Clinical AI Chat

Visit reachrx.ai ↗
LLMsRAGEmbeddingsVector SearchData PipelinesWeb AppiOS App

1M+

Clinical sources indexed

500K+

Chat uses

30K+

Prescribers

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.