Your AI system clears FDA.
Now prove it's worth paying for.
FDA clearance and CMS reimbursement ask completely different questions with completely different evidence standards. We build the post-deployment evidence architecture that answers both — for payers, for procurement, and for every room where health AI deals are decided.
What's missing from most deployments
"We don't just assess AI systems — we define what they need to prove."
Not a report. A working evidence architecture your team owns.
Most governance engagements produce documentation. A RIGOR™ Assessment produces an evidence system — live, operational, and permanently owned by your team. The difference is structural: we define what your AI must prove, then build the infrastructure to prove it.
Evidence Gap Analysis
Where your AI is exposed — before regulators or payers find it first
Domain-by-domain gap map across all five RIGOR™ modules. You see exactly where evidence is missing, what that means for reimbursement and procurement, and what it takes to close it.
Evidence Architecture
The system that generates evidence continuously — not a one-time audit
Operational documentation, monitoring infrastructure, and accountability structures your team runs independently. No ongoing dependency on us — you own it permanently from day one.
Regulatory Positioning
Mapped to FDA–EMA Good AI Practice and 2026 CMS reimbursement codes
Organizations with post-deployment real-world evidence will bill under new AI-enabled CPT codes from day one. Organizations with only pre-deployment validation data will not. The RIGOR™ architecture determines which category you occupy.
Audit Readiness
Produce a complete audit trail in 30 days — before anyone asks for it
Only 22% of health system leaders can do this today. The RIGOR™ architecture makes it the default — not a fire drill. Every AI decision is documented, traceable, and defensible before a regulator or plaintiff's attorney asks first.
Not Another Consulting Engagement.
A System Your Team Owns Permanently.
Deloitte, McKinsey, and KPMG sell you a report. Every new question requires another engagement. Every personnel change resets institutional knowledge. The RIGOR™ System is built differently — every engagement produces documentation, protocols, and evidence architecture your team operates independently, indefinitely.
| Traditional Big 4 Consulting | RIGOR™ System | |
|---|---|---|
| Ownership | ✕ Knowledge walks out when the engagement ends. | ✓ Your team owns the system, documentation, and protocols permanently. |
| Deliverable | ✕ Slides and recommendations. No ongoing evidence production. | ✓ Evidence architecture, governance documentation, runtime monitoring protocols — all operational. |
| Payer evidence | ✕ Report satisfies regulators. Does not generate CMS reimbursement evidence. | ✓ Operational Proof and Runtime Monitoring generate evidence for regulators AND payers simultaneously. |
| Cost model | ✕ Ongoing retainers. Every change requires another engagement. | ✓ Fixed-scope initial engagement. Your team runs it independently after handoff. |
| Speed | ✕ Months of back-and-forth before anything deployable. | ✓ Gap analysis in weeks. Evidence roadmap immediately actionable. |
| Sector depth | ✕ General AI governance applied to healthcare. | ✓ Built from clinical AI validation science. Developer also built a validated clinical AI product from scratch. |
"Most companies treat this as a compliance problem. It's actually a system design problem. We don't just assess AI systems — we define what they need to prove."
RIGOR™ System Evidence Architecture
A fixed-scope engagement that produces a governance gap analysis, evidence architecture roadmap, and deployment-ready documentation your team owns permanently. Designed for organizations with AI already deployed — and for organizations building AI that needs to be defensible from day one.
What the RIGOR™ System Engagement Produces
Not recommendations to implement later. Evidence architecture your team operates now.
What We Analyze
What You Receive
Who this is for: MedTech companies post-FDA clearance without a reimbursement pathway. Health systems with deployed AI and no audit trail. Organizations entering hospital AI procurement. Regulated enterprises building AI in high-consequence environments.
Runtime Monitoring Is Not
What You Think It Is.
Most AI systems track statistical drift. The RIGOR™ System's Runtime Monitoring module captures something harder and more commercially valuable: impact evidence.
What most systems track
Statistical drift in model outputs. Whether accuracy metrics have shifted from baseline. Alerts when the distribution changes.
What RIGOR™ tracks
Whether outputs are accepted or overridden by clinicians. How decisions actually change. What outcomes follow. The evidence chain from AI recommendation to patient result.
Why it matters commercially
This is the data required for CMS reimbursement renewal, D&O insurance governance endorsements, hospital contract renewals, and the legal audit trail.
The FDA's January 2025 draft guidance on AI-enabled device software functions and the September 2025 Joint Commission and CHAI guidance both mandate post-deployment surveillance as a regulatory requirement. D&O insurance carriers are moving toward mandatory governance endorsements — hospitals without institutional governance documentation already face premium increases and coverage exclusions. Runtime Monitoring, designed correctly, satisfies both from the same data stream.
Selected Over Amazon, Microsoft,
IBM, SAS, NTT Data, Dell, and Oracle.
The RIGOR™ methodology was evaluated against multiple major enterprise AI vendors in a competitive selection process for a Fortune-class industrial company. RIGOR™ was the only framework to address the full problem scope without introducing cloud dependency, cost overrun, or data sovereignty risk.
AI-Driven Early Warning System — Fortune-Class Industrial Company
The Problem
A Fortune-class industrial company faced persistent degradation in early warning signal quality — the intelligence used to detect product failures, manage warranty reserves, and meet regulatory reporting requirements across multiple jurisdictions.
Legacy infrastructure, siloed data across divisions, and no real-time early warning capability created compounding financial and regulatory exposure at scale.
Multiple major enterprise AI vendors evaluated the problem. None addressed the full scope without introducing significant cloud dependency, implementation cost, or data sovereignty risk.
The RIGOR™ Approach
Requirements first: Stakeholder objectives formally scoped across regulatory compliance, product lines, and supply chain operations before any architecture decisions were made.
Implementation: Edge-first, on-premises architecture eliminating cloud dependency. Modular design enabling division-specific deployment without system-wide risk.
Operational Proof: Live demonstration in the actual deployment context — not a controlled lab environment. Evidence produced under real operational conditions, not simulated ones.
"The only team that understood the full problem — not just the AI layer."— Client executive stakeholder, competitive selection process
The transferable principle: The core problem is identical across high-stakes industries — consequential decisions on incomplete, siloed signal where the cost of failure is asymmetric. RIGOR™ is not sector-specific. The methodology transfers to any environment where AI makes decisions that must be defended.
Questions About RIGOR™ System and AI Evidence Architecture
What is the RIGOR™ System?
The RIGOR™ System is a complete AI governance and validation lifecycle system developed by Dr. Olga Lavinda at Health AI LLC. Five sequential modules — Requirements, Implementation Architecture, Governance, Operational Proof, and Runtime Monitoring — generate the evidence AI systems must produce to create commercial value. Unlike governance frameworks that describe what organizations should do, the RIGOR™ System defines what evidence their AI must generate and builds the architecture to produce it continuously after deployment.
What is the gap between FDA clearance and CMS reimbursement for AI medical devices?
FDA and CMS ask fundamentally different questions. FDA asks whether a device performs as intended without undue risk — answered by technical validation and pre-deployment performance metrics. CMS asks whether the device improves clinical outcomes, reduces costs, or replaces existing billable services — answered by health economic studies and real-world outcomes from actual deployment. These evidence standards are orthogonal. A company can achieve FDA 510(k) clearance and have zero reimbursement-qualifying evidence. The RIGOR™ System's Operational Proof and Runtime Monitoring modules generate evidence that satisfies both audiences simultaneously.
Why is AI governance a revenue strategy rather than a compliance cost?
AI governance generates commercial value through three mechanisms. First, runtime monitoring produces the real-world outcomes data that payers require for reimbursement coverage decisions — organizations with this evidence can bill under new 2026 CMS CPT codes for AI-enabled services. Second, governance documentation closes hospital procurement contracts: the questions that kill health AI deals at the contract stage are almost entirely governance questions, not performance questions. Third, D&O insurance carriers are moving from voluntary questionnaires to mandatory governance endorsements — hospitals without documented governance face premium increases and coverage exclusions.
How is RIGOR™ System different from Big 4 AI consulting?
Traditional consulting firms sell governance reports. Every new question requires another engagement. The RIGOR™ System produces documentation, protocols, evidence architecture, and runtime monitoring systems that your team operates independently, permanently. The deliverable is not a report. It is a working system that continuously generates evidence for regulators, payers, and procurement reviews without ongoing consulting dependency.
What does Runtime Monitoring produce that most AI monitoring misses?
Most monitoring tracks statistical drift — whether accuracy metrics have shifted. The RIGOR™ System's Runtime Monitoring captures impact evidence: whether AI outputs are accepted or overridden by clinicians, how decisions actually change, and what patient outcomes follow. This is the data required for CMS reimbursement renewal, D&O insurance governance endorsements, hospital contract renewals, and legal audit trails.
Who developed the RIGOR™ System?
The RIGOR™ System was developed by Dr. Olga Lavinda, CEO and founder of Health AI LLC. Dr. Lavinda's background spans molecular pharmacology, chemometrics, and 15 years in translational science, with NIH NRSA fellowship training. She is a member of the Coalition for Health AI (CHAI) and an Assistant Professor of Chemistry and Biochemistry. She is the only AI governance system developer who has also built and validated a consumer clinical AI product from scratch — Clarity — demonstrating that what the RIGOR™ System describes is buildable, not theoretical. Health AI was selected over Amazon, Microsoft, IBM, SAS, NTT Data, Dell, and Oracle for a major enterprise AI engagement.
Find out what evidence
your AI system is missing.
A RIGOR™ Assessment maps your current deployment against the five modules and identifies the specific gaps blocking reimbursement, procurement, or regulatory defensibility. Your team owns the findings.
Goes directly to Dr. Lavinda. No CRM, no drip sequence.
Or reach out directly: healthai.com/contact · lavinda@healthai.com
Ready to build AI that
can defend itself in production?
Health AI works with healthcare organizations, medtech companies, and regulated enterprises to implement the RIGOR™ System. Engagements are fixed-scope and produce deliverables your team owns permanently.
Download the RIGOR™ System Playbook (free) · Take the Readiness Assessment
RIGOR™ is a trademark of Health AI LLC · healthai.com · RIGOR™ Framework · Clarity · CityOS™
Olga Lavinda, PhD · CEO, Health AI LLC · New York, NY · © 2026 Health AI LLC
Health AI LLC is a U.S.-based AI validation science firm. Not affiliated with HealthAI — the Global Agency for Responsible AI in Health (healthai.agency).