Clinical AI That Survives Deployment
Eight weeks from AI use case to a production clinical system, with PHI-safe architecture and an AI Safety Layer designed and signed off before a single line of product code is written.
Most healthcare AI doesn't fail at the idea stage. It fails at deployment. Models that work in a notebook collapse under compliance review, PHI exposure risk, latency at scale, and the unpredictability of a real clinical workflow. We design against those failure modes first and build the product second, because retrofitting safety after launch is the pattern that kills clinical AI.
8 weeks Fixed timeline, fixed scope |
Week 2 Safety layer before product code |
Client-owned All AI runs in your AWS accounts |
4 weeks Post-launch warranty |
The demo works. The deployment doesn't.
Getting an LLM to produce something impressive on clinical data is now straightforward. Getting it to behave predictably on real patients, under a compliance review, at production volume, is a different and much harder problem, and it's the one that stops products from shipping.
The failure modes are consistent. PHI reaches a third-party model because the boundary was a prompt instruction rather than a technical control. Outputs that looked reliable in testing contradict clinical fact on inputs nobody thought to try. Cost and latency explode the moment traffic is real, because nothing routes or caches. And when the hospital's IT team asks for an audit trail of every AI interaction, there isn't one, because logging was a Phase 2 item.
None of these are model problems. They're architecture problems, and architecture is the thing most AI builds skip in order to get to a demo faster.
By the time a team discovers this, the product exists. The safety work then means rebuilding the data layer, the orchestration and the audit path underneath a live product, which is the expensive version of the same work.
A system, not a wrapper
Two terms get used interchangeably and shouldn't be. An AI wrapper is a thin layer of model API calls over existing data. A clinical AI system has orchestration, a structured data layer, a hard PHI boundary, guardrails and clinical validation as foundational architecture. Most AI demos are wrappers. Most wrappers fail in production.
Built in Week 2, before any product feature
The AI Safety Layer is compliance and trust infrastructure, and it goes in before the product does. Every feature built in Weeks 3–6 inherits it automatically.
PHI boundary enforcement
A hard technical separation between patient data and model inputs, implemented at the data layer rather than as a prompt-level instruction. PHI is de-identified, redacted, or routed through BAA-covered APIs before it reaches any model.
Hallucination mitigation
Domain-specific output validation. Responses that contradict known clinical fact, or that would produce harmful guidance, are blocked before they reach a user. Retrieval grounds outputs in verified clinical knowledge, and confidence thresholds route low-confidence outputs to human review rather than surfacing them.
Clinical guardrails
Rules-based and model-based filters configured for your specific clinical domain: emergency escalation triggers, medication safety checks, and the domain-specific constraints a general-purpose filter won’t know about.
Prompt logging and audit trail
Every prompt and completion logged immutably, stored separately from application data, retained per HIPAA requirements. Every AI-generated output traces back to the inputs, model version and timestamp that produced it.
BAA-ready model infrastructure
Every model API and self-hosted inference service configured to support BAA coverage. No PHI passes through an AI service that isn’t BAA-eligible.
Fallback and degradation handling
The clinical workflow does not break when the AI layer is slow, unavailable, or behaving unexpectedly. Graceful degradation is a designed behavior, not an incident.
Trust isn't a wrapper. It's the foundation. Every feature built after Week 2 inherits the safety architecture, because retrofitting it is what causes clinical AI to fail.
Architecture first. Product second. Validation before go-live.
Sign-off on the Week 1 architecture document and the Week 2 safety layer are both required before the build begins. That's deliberate: it's the only point where changing the foundation is cheap.
Your cloud, your accounts, from day one
Every model, inference service and orchestration layer runs in your AWS accounts. We configure and deploy it during the engagement; you own it outright at handover.
This isn't a technicality. If the AI infrastructure runs on our cloud, you inherit our uptime, our security posture and our BAA compliance as dependencies. Deploying on your own infrastructure removes all three. Your BAA covers your own cloud. Your IT team can audit it. Your enterprise prospects can review it without us in the room.
How models are deployed
Five categories we architect for
The domain determines the architecture: data structures, model selection, guardrail design and how validation is run all change with it.
Intake and triage
Front-door routing, symptom intake, eligibility and prior-auth automation. Systems that qualify and route patients at the entry point to care.
Decision support
Clinician-facing AI that surfaces evidence, refines notes and flags risk in real time, embedded in the workflow, not a standalone tool.
Remote monitoring
Streaming wearable and device data, anomaly detection, and intervention loops for chronic and post-acute care.
Patient engagement
Workflow-embedded conversational AI for adherence, education and care navigation. PHI-safe and validated for real patient populations.
Data infrastructure
The structured longitudinal data layer that makes every other AI product possible: ETL, clinical knowledge bases, vector stores.
What we build on
Standardised for PHI safety, ecosystem maturity, and whether your own team can maintain it after handover.
Who this is for
This engagement fits if you:
Four buyer profiles
Founders
A first clinical AI product with a foundation that survives V2: speed, plus a system that doesn’t collapse in a health system demo.
Startups
Past V1 and scaling. A prototype that demos well but fails in real clinical environments.
Health systems
Workflow-embedded AI that integrates with existing infrastructure rather than isolating from it, with full audit capability.
Research teams
Models, papers and pilots that need the production engineering and compliance layer to become deployed systems.
Best-fit signal. The clearest one: you can articulate exactly what the AI should do clinically, and you've discovered that making it safe, compliant and reliable in production is a harder problem than building the model was.
Where we'd point you instead
Want to add AI features to an existing healthcare app? 8-Week HIPAA-Compliant MVP Launch → or a Healthcare Product Pod →
Can't yet describe what the AI should do, or what data it needs? Validation has to come before system design. Talk to us → about scoping that first.
Need custom model training or fine-tuning? A separately scoped ML engagement. This build uses pre-trained public and open-source models only.
Need an investor demo rather than a clinical deployment? 8-Week HIPAA-Compliant MVP Launch →. The cost of this engagement isn't justified for a prototype.
Need SOC 2, HITRUST, or direct Epic/Cerner production integration? A separately scoped enterprise engagement. Talk to us →
Are building outside healthcare? Our entire process is built around healthcare compliance and clinical workflow. Not a fit, and we'll say so on the first call.
One hard requirement: the Weeks 1–2 architecture sessions need 3–5 working sessions with your clinical and technical stakeholders. If your team can't be available in those two weeks, the engagement doesn't work, and that's the one dependency we can't absorb.
The Week 8 acceptance checklist
Nothing is declared complete until every item below is verified in the handover call. The warranty starts only after both sides sign off.
The system
- Clinical AI system deployed in production on your AWS.
- All AI features operational with PHI-safe data flows and guardrails active.
- Orchestration live: routing, retrieval, caching, fallback.
- EHR aggregator and wearable integrations live where in scope.
- Clinical UI embedded in the target workflow.
The safety layer
- PHI boundary implemented and tested.
- Hallucination mitigation deployed and verified.
- Clinical guardrails configured and tested for your domain.
- Prompt logging live.
- Audit trail active across every AI interaction.
- BAA-ready model infrastructure confirmed.
Security and compliance
- PHI encryption at rest (AES-256) and in transit (TLS 1.3), verified.
- RBAC configured and tested for every defined role.
- HIPAA risk assessment covering the system's PHI handling.
- BAA register with every PHI-processing vendor confirmed.
- AI data flow document mapping PHI through every component.
- Clinical validation report.
- Security review report.
Code and documentation
- Full codebase in Git with your team holding admin.
- Architecture document as built.
- AI Safety Layer specification: how each component works and is configured.
- API documentation.
- Model selection rationale: which models, why, and how.
- Deployment and infrastructure guide.
The handover itself
- All cloud credentials and accounts transferred to you.
- All third-party AI service accounts transferred or shared.
- A 60-minute walkthrough of the system, the safety layer and the documentation.
- Then the warranty window opens.
We hold no access to your AI infrastructure or data after handover unless you engage us again.
Four-week warranty, then a choice
For 28 days after go-live, any AI behavior issue or compliance gap in the delivered system is resolved at no cost. An AI behavior issue means output that contradicts the clinical validation report or violates the PHI boundary, not a new capability request. PHI exposure or a system-down issue is addressed within 4 business hours; a safety layer failure or major broken feature within 24.
Then:
The system is live and you need ongoing AI capability, model updates, compliance maintenance and new clinical features. A Healthcare Product Pod →: the team that built it keeps developing it, on a monthly subscription.
Enterprise hospital conversations need SOC 2, HITRUST prep, or direct EHR production integrations. A separately scoped enterprise engagement: the enterprise layer on top of what exists.
You want a second AI system, or a new clinical domain. A second build. Architecture decisions from the first one accelerate it.
The data layer, orchestration and compliance documentation are all designed with those paths in mind, so continuing doesn't mean rebuilding.
What this engagement doesn't cover
Frequently asked questions
Relevant Case Studies
CareCortex — End-to-End AI Operating System for Healthcare
AI workflow layer integrated with existing EHR — real-time clinical decision support, AI-assisted note generation, routine task automation, and seamless data access. PHI-safe from day one.
SkinSAFE by Mayo Clinic — Science-Backed Skincare Safety Platform
Long-term embedded engineering partnership with Mayo Clinic — 180+ production builds shipped to tens of thousands of active users, with zero disruptions to the live platform across the entire 2+ year engagement.
AuraFem — Holistic Women's Health & Practitioner Platform
Rescued a crashing FlutterFlow app with a zero-downtime migration to HIPAA-compliant Flutter — then built a full practitioner portal in the same engagement. 15 practitioners onboarded on day one, first payment in 5 days.
Bring the use case. We'll tell you what it takes to deploy it.
If you can describe what the AI should do clinically, a scoping call is enough to tell you whether eight weeks is realistic, what the architecture would need to look like, and where the compliance work concentrates.