RAG systems for Healthcare.
Production-grade RAG engineered for healthcare and life sciences.
SmartDuke builds rag systems for healthcare and life sciences — systems that ground AI answers in your own data, with citations and freshness guarantees. Clinician burnout is the largest unbilled cost in healthcare. AI that compresses documentation time without sacrificing audit-readiness is the obvious fix. Our approach: Retrieval evals before any LLM call, citation-rate monitored as a first-class metric, and explicit refusal when confidence falls below threshold.
The problems
we keep solving.
Clinician burnout is the largest unbilled cost in healthcare. AI that compresses documentation time without sacrificing audit-readiness is the obvious fix.
Documentation burden eats clinical time
Clinicians spend up to two hours on documentation for every hour with patients — directly contributing to burnout.
Patient triage queues grow faster than staffing
Front-line teams are overwhelmed; severity routing is inconsistent; high-acuity patients sometimes wait too long.
Medical literature volume is unmanageable
Keeping current with relevant research is structurally impossible for any individual clinician at scale.
Three things we'd build
first.
Concrete starting points for rag systems in healthcare and life sciences. We pick the one with the highest leverage and the cleanest measurement story.
- Idea 01
Clinical-note copilot that drafts encounter notes from voice or structured input, with audit-ready citations
- Idea 02
Triage agent that scores incoming requests and routes by acuity with explainable reasoning
- Idea 03
Medical-literature RAG with source-priority ranking on peer-reviewed and guideline content
Production-grade,
from day one.
Hybrid retrieval (BM25 + semantic + reranker), source-priority ranking, freshness gating, and answer-capsule generation with sentence-level citations.
Evals before launch.
Every loop, tool call, and structured output is graded with a frozen test set and an explicit rubric. Failed evals block the deploy.
Telemetry from day one.
Traces, latency budgets, token costs, and error rates wired up before the first user touches the system.
Guardrails as architecture.
Input validation, output verification, escape hatches, and human handoff paths designed in — not bolted on after incidents.
Boring stack on the edges.
Cutting-edge model in the middle. Reliable infrastructure around it. Stability where it earns its place.
- ×Hallucination on long-tail queries
- ×Stale documents silently used
- ×Missing or wrong citations
- ×Retrieval that fails on the queries that matter most
Common questions.
01How long does it take to build rag systems for Healthcare?
How long does it take to build rag systems for Healthcare?
Discovery is one week. A working prototype (Spark) is 2–3 weeks. Full production Build for healthcare and life sciences typically runs 8–12 weeks depending on data complexity, integrations, and compliance scope. We commit to a precise timeline at quote stage.
02What does pricing for rag systems typically look like?
What does pricing for rag systems typically look like?
Every engagement is scoped to outcomes, not hours. Discovery starts in the low four figures. Spark and Build are priced per project. Embed retainers are monthly. We return a quote within 24 hours of inquiry.
03Can you take over an existing RAG project that's stalled?
Can you take over an existing RAG project that's stalled?
Yes — it's a common engagement. We review what's there, tell you honestly what stays and what we'd rebuild, then ship it to production. Healthcare engagements often start this way.
04What's different about your approach to rag systems?
What's different about your approach to rag systems?
Retrieval evals before any LLM call, citation-rate monitored as a first-class metric, and explicit refusal when confidence falls below threshold. We hold the same engineering bar across every engagement, regardless of industry — but the specifics for healthcare and life sciences are tuned to your trends and pain points.
Same capability, different industry.
Same industry, different capability.
Have an AI product
that needs to ship?
Tell us where you are — early concept, broken prototype, or scaling something that already works. We'll come back within 24 hours with a take and a quote.