// Industry
Healthtech AI development
AI for clinical and healthtech products, where a wrong answer isn't a bad UX — it's a safety issue. Grounded, evaluated, and auditable.
// The problem
Why this is hard
In healthtech, a plausible-but-wrong answer isn't a UX bug — it's a clinical risk. The hard part isn't calling a model, it's proving the output is faithful to the source, keeping patient data private, and leaving an audit trail a regulator (and a clinician) will trust. Demos that skip that never make it past review.
// What matters here
The capabilities that move the needle
RAG & Knowledge Systems
Answers grounded in your clinical corpus with citations back to the source — the difference between a demo and something a clinician can act on.
LLM Integration & Evals
Every prompt and model change gated on an eval suite, so faithfulness is measured, not assumed — the bar a safety-critical product needs.
AI Agents & Automation
Workflow automation with human-in-the-loop review, so the ambiguous cases route to a person instead of an unattended guess.
MLOps & Infra
Observability, uptime, and cost controls for a system that has to be reliable when real patients depend on it.
// Deep dives
Going deeper for healthtech
// 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
// Related
Fintech
AI for fintech — document intelligence, automation, and decision support that hold up to an audit, not just a demo.
Insurance
AI for insurance — claims, underwriting, and document automation that are accurate, explainable, and auditable.
// Let's build
Building AI for healthtech?
Tell us where you are. We reply within a day with a concrete next step.