// Healthtech × RAG & Knowledge Systems
RAG for healthtech
RAG for healthtech — retrieval grounded in your clinical corpus with citations and faithfulness evals, so answers are safe to put in front of a clinician.
// The problem
Why this is hard
In a clinical product, a retrieval system that returns a plausible-but-unsupported answer isn't a bad search result — it's a safety event. Generic RAG isn't enough: you need grounding in your clinical corpus, citations a clinician can check, an eval harness that measures faithfulness, and privacy that keeps patient data where it belongs.
// How we do it
The approach for this fit
Clinical-grade grounding
Answers composed only from your retrieved clinical sources, with citations back to the exact passage — no unsupported claims reach a clinician.
Faithfulness evals
Every change is scored for citation support on a held-out set; an answer the source doesn't back fails the gate rather than shipping.
Privacy + audit
Run retrieval in your environment (or under your data terms) with every step traced, so patient data stays private and each answer is reconstructable.
Human-in-the-loop
Low-confidence or high-stakes cases route to a person with the retrieved context attached, so automation is bounded to what it does reliably.
// Proof
Shipped in production
Healthtech — Clinical RAG
hallucinated citations in eval
on the citation eval set
source-linked answers
every shipped answer
// FAQ
Common questions
// Part of
// Let's build
Building RAG for healthtech?
Tell us where you are. We reply within a day with a concrete next step.