Clinical AI Reliability
Your clinical AI works. Mostly.
The demo works. The pilot looks good. But you still don't know how it behaves under real clinical pressure. We find out before your users, buyers, or clinicians do.
Clients & Partners
Trusted by healthcare teams
who can't afford to be wrong
What we do
Know where it breaks before someone else does.
We test your clinical AI the way it'll get tested in the real world, then show you exactly what to fix and in what order.
Sam Morhaim · 25 years building healthcare software · Clinical AI in production today
Catch the wrong answers first
We test the messy questions your users will actually ask, not the clean ones in your demo. You see the failure modes before they cost you a deal or a customer.
- Hallucination behavior
- Contradictory evidence
- Real clinician queries
- Edge case prompts
See where patient data really moves
PHI rarely stays where you think. We map the real path through prompts, logs, vendors, and traces, so nothing surprises you later.
- Prompt and context exposure
- Log and trace audit
- Third-party vendor calls
- Source-to-output traceability
Know what to fix first
Not every issue is urgent. You get findings ranked by severity with specific recommendations, clear enough for engineering and honest enough for a customer call.
- Severity-ranked findings
- Evidence for each issue
- Concrete remediation steps
- Procurement-ready answers
Fix what needs fixing
When the work is bigger than a report, we stay on. Same team that found the problem, with senior engineers who've built this in production.
- Retrieval pipeline rework
- Logging and audit infrastructure
- Evidence ranking systems
- Production-grade observability
How we work
A repeatable way to know
what's actually going on.
We do this the same way every time. Not because we like processes, but because clinical AI breaks in patterns, and a repeatable approach catches more than a custom one.
ClearMap
See what you actually have
Before we test anything, we map it. Data flows. Where PHI moves. How the AI is structured. What it touches. Where the risk sits. You get a clear picture of your system, in language a CTO can act on and a customer can read.
3D Method
Build what’s missing, in the right order
When findings need engineering, we don’t theorize. We rebuild the fragile parts the same way we’d build them in our own production systems. Architecture first. Test as we go. Done means it actually works, not just that we shipped it.
PulseLayer
Know it still works next month
The hardest part of clinical AI isn’t shipping it. It’s knowing it still works six weeks after launch. PulseLayer is how we instrument your system so you can see what it’s doing — retrieval quality, output consistency, PHI flow, drift. The things that quietly go wrong before they loudly go wrong.
The Work
From mostly works to
actually works.
We find what's broken, then fix what matters. End to end.
- Find it
We get inside your system
- Test what breaks under real use
- Map where PHI actually moves
- Surface the gaps that block deals
- Show you
You see what's actually broken
- Clear system map
- Findings ranked by what hurts
- Specific fixes, in order
- Answers you can use with customers
- Fix it
We stay on and make it right
- Senior engineers, same team
- Retrieval, logging, observability
- Production-ready, not prototypes
Reliability Engagement
Find the gaps. Fix what matters. Ship with more confidence.
- Assessment
- From $7,500
- Engineering
- Scoped after findings
What clients say
Trusted by teams who needed someone
who'd actually been there.
The people we work with are building real systems for real patients and real clinicians. They didn't need another consulting deck. They needed someone who had already seen what breaks inside real healthcare systems and knew where to look first.
"A unique combination of skills and an amazing team. Throughout the project, they never missed a deadline."
"Sam and his team move fast, communicate clearly, and bring strong technical judgment to complex healthcare AI work."
Recognition
Recognized for the work,
not the marketing.
We've been recognized for software development and healthcare technology work. But the work that matters most is quieter: systems that keep working, security questions with real answers, and engineers who stop getting pulled into the same fire drills.
Selected Work
Clinical AI we've built, validated, or kept alive.
A few of the systems we've worked on across healthcare AI, clinical decision support, and high-trust environments.
CLINICAL DECISION SUPPORT · FUNCTIONAL & LONGEVITY MEDICINE
Built and still help run the architecture, engineering, and infrastructure behind FunctionalMind, a clinical decision support platform that uses evidence retrieval, RAG, and lab data to give clinicians grounded AI answers. The system handles real lab files in all their messy variety, ranks evidence by quality, and traces every answer back to a source. HIPAA and GDPR from day one. Observability built in. Built for the messy reality of clinical data, not a clean demo environment.
- Evidence retrieval and RAG pipeline
- Clinical LLM integration
- Lab data ingestion under real-world variability
- HIPAA and GDPR infrastructure
- Telemetry, observability, audit logging
- Ongoing fractional CTO leadership
WOMEN'S HEALTH · PREDICTIVE ANALYTICS FOR MENOPAUSE FORECASTING
Took this client from a PoC MVP to a production-ready women's health platform built around predictive ML models for menopause forecasting. Led technical direction, rebuilt the infrastructure for scale, and drove the compliance work needed to pass HIPAA audit checks — so the team could focus on growth, not firefighting.
- MVP to production infrastructure rebuild
- Predictive ML model integration
- HIPAA compliance audit and remediation
- Scalable backend architecture
- Technical direction and delivery support
HEALTH & LIFE INSURANCE · MOBILE + WEB PLATFORM
Four years and counting. Built and scaled the mobile and web applications that power Olé Life's agent and customer experience — including real-time health and life insurance quoting, policy management, and bilingual workflows across both platforms. Expanded into analytics, observability, and continuous process improvement as the business scaled.
- Real-time insurance quoting (mobile + web)
- Agent and member-facing application architecture
- Analytics, telemetry, and observability
- Bilingual platform support
- Ongoing delivery and process improvement
MARITIME HEALTHCARE · CASE MANAGEMENT + TELEMEDICINE PLATFORM
Built SeaCare's healthcare case management platform from the ground up for maritime operations — where connectivity is limited and getting care wrong has real consequences. Delivered onboard crew health workflows, telemedicine coordination, and peer-to-peer live video and audio for remote medical assist.
- Healthcare case management platform
- Onboard crew workflow design and build
- Peer-to-peer live video + audio (telemedicine)
- Remote tele-assist coordination
- Mobile + operational platform architecture
ADAPTIVE LEARNING PLATFORM · DYSLEXIA INTERVENTION
Architected and built Dysolve's interactive learning platform — a scalable, therapy-oriented system designed to support dyslexia intervention through AI-driven gamification and adaptive content. Built to grow with the program, not just demo well.
- Learning platform architecture and build
- AI gamification engine
- Adaptive, therapy-oriented user flows
- Scalable content and session infrastructure
- HTML5 interactive engine
Questions you probably have
The things people ask
before they book.
If you don't see your question here, book a call and ask. It's a conversation, not a sales pitch.
What do you actually do?
We find where your clinical AI breaks before someone else does. That usually means testing how it behaves on hard questions, mapping where patient data really moves, checking whether retrieval is pulling the right context, and finding the gaps that would show up in a security review or a clinician's first complaint. You get a clear report with what to fix and in what order. If the fixes are bigger than your team can take on, we can stay and do the work with you.
Who's this for?
Healthcare teams who've built something with AI and want to know it actually works. Most of our clients are using LLMs, RAG, or some kind of language model with clinical data. They've usually got a working product or pilot and a growing sense that the gap between "it works" and "I'd bet the company on it" needs to close.
Do I work with Sam directly?
Yes. Sam runs the assessment, makes the calls on architecture and validation, and writes the findings. If the work expands into engineering, our senior team comes in. You're not getting handed off.
What is the assessment, exactly?
Two weeks. We get inside your system, test how it behaves, map the data flows, look at the architecture, and write up what we find. You get a system map, a findings report with severity and evidence, and a plan you can actually execute. Some clients stop there. Some keep us on to do the engineering work. Both are fine.
How is this different from a HIPAA audit?
HIPAA audits check whether you're compliant. We check whether your AI works. There's overlap, but they answer different questions. A HIPAA audit won't tell you your retrieval is broken. We will. And we'll show you where patient data is leaking that the audit didn't catch.
Can you look at our RAG or LLM setup?
That's most of what we do. We test how your system finds evidence, handles conflicts between sources, builds context, generates answers, and behaves on the kinds of questions clinicians actually ask. If something's wrong, we'll find it.
We already built it. Is it too late?
Honestly, that's the best time to bring us in. If you have a working product or pilot, there's something real for us to test. We'd rather find the problems now than have a customer or clinician find them later.
Do you only do reviews, or do you build too?
Both. The assessment is usually the entry point. Some clients just need the report and a plan. Others want us to stay on and do the engineering. We do the work when it makes sense, and we don't push it when it doesn't.
How fast can we start?
Usually within a week or two of the first call. The assessment itself takes two weeks.
What makes you different?
We've built clinical AI in production. Not as advisors. As the team that ships it. FunctionalMind is one of ours, still running, still in clinical use. When we test your system, we're testing it the way we'd test our own. That's a different conversation than what you'd get from a consultant who's only read about this work.
Ready when you are
Find out
before it matters.
You've built something real. Now find out where it's solid, where it's fragile, and what to fix next.
Reliability Assessment