SmartDuke Technologies
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AI internal tools for Healthcare.

Production-grade internal tools engineered for healthcare and life sciences.

In brief

SmartDuke builds ai internal tools for healthcare and life sciences — systems that replace manual back-office workflows with audited, AI-powered operating tools. Clinician burnout is the largest unbilled cost in healthcare. AI that compresses documentation time without sacrificing audit-readiness is the obvious fix. Our approach: We design tools with the operators, not for them. Workflow-first. Audit trail mandatory. Eval coverage on every action class.

Why healthcare and life sciences are doing this now

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.

01 / 03

Documentation burden eats clinical time

Clinicians spend up to two hours on documentation for every hour with patients — directly contributing to burnout.

02 / 03

Patient triage queues grow faster than staffing

Front-line teams are overwhelmed; severity routing is inconsistent; high-acuity patients sometimes wait too long.

03 / 03

Medical literature volume is unmanageable

Keeping current with relevant research is structurally impossible for any individual clinician at scale.

Use cases

Three things we'd build
first.

Concrete starting points for ai internal tools in healthcare and life sciences. We pick the one with the highest leverage and the cleanest measurement story.

  1. Idea 01

    Clinical-note copilot that drafts encounter notes from voice or structured input, with audit-ready citations

  2. Idea 02

    Triage agent that scores incoming requests and routes by acuity with explainable reasoning

  3. Idea 03

    Medical-literature RAG with source-priority ranking on peer-reviewed and guideline content

Outcome metric → Documentation time per encounter, triage accuracy, and audit-trail coverage
How we engineer it

Production-grade,
from day one.

Workflow-first design, structured input forms, AI in the middle of the loop, role-based access controls, and full audit logs of every action.

01 /04

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.

02 /04

Telemetry from day one.

Traces, latency budgets, token costs, and error rates wired up before the first user touches the system.

03 /04

Guardrails as architecture.

Input validation, output verification, escape hatches, and human handoff paths designed in — not bolted on after incidents.

04 /04

Boring stack on the edges.

Cutting-edge model in the middle. Reliable infrastructure around it. Stability where it earns its place.

Common failure modes we engineer against
  • ×Poor adoption when AI replaces the wrong step
  • ×Brittle integrations that break silently
  • ×No audit trail, leading to compliance issues
  • ×Tools that demo well but no one uses
Next.js 16SupabaseClaude Sonnet 4.6Postgres RLS
FAQ · 04

Common questions.

01

How long does it take to build ai internal tools 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.

02

What does pricing for ai internal tools 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.

03

Can you take over an existing internal tools 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.

04

What's different about your approach to ai internal tools?

We design tools with the operators, not for them. Workflow-first. Audit trail mandatory. Eval coverage on every action class. 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.

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