Software-driven risk
For software, SaMD and AI/ML-enabled medical devices, with public FDA records supporting the review of existing FMEA and hazard-analysis files.
FMEAi / PRODUCT CASE STUDY
Evidence-backed AI. Human-led decisions.
FMEAi is an AI risk intelligence product built by HighCloud for medical-device and energy risk workflows. It shows how source-linked evidence, AI-assisted analysis and human review can work together in a practical product.
Explore FMEAi (opens in a new tab)
FMEAi is owned by HighCloud.
A practical AI product case study.
THE CHALLENGERisk files can miss
Risk files can miss
what the evidence reveals.
Medical-device teams need to assess evolving failure modes against a growing public evidence base. Manual reviews can make it difficult to identify relevant signals and keep findings traceable.
THE APPROACHA second opinion.
A second opinion.
Not a replacement for judgment.
FMEAi compares existing FMEA and hazard analysis files with AI-derived failure modes and public FDA records, then presents evidence-linked findings for a team to review.
PURPOSE IN PRACTICE
Find the gap.
Before it becomes harm.
FMEAi’s mission is to advance patient safety by preventing harm before products reach patients. Its medical-device profile focuses on software, Software as a Medical Device (SaMD), and AI/ML-enabled devices.
Built for these software-driven devices, FMEAi works alongside existing risk tools rather than requiring a QMS migration.
The medical-device profile uses FDA MAUDE, recalls, enforcement actions and 510(k) records. The energy profile uses PHMSA incident data, NTSB reports and state commission records.
Read the product detailsThe mission behind FMEAifmeaiEVIDENCE → CONFIDENCE
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START WITH YOUR EXISTING FILEFMEA & hazard analysis
INPUTEvidence-backed AI draft
Every finding links to its source.
MAUDERecalls510(k)Enforcement
People make the decision.Engineer review · Separate risk approval
Illustrative medical-device workflow · not a product screenshot
ONE ENGINE. INDUSTRY-SPECIFIC CONTEXT.
Evidence changes.
Accountability doesn’t.
The current public FMEAi site describes two industry profiles sharing import, gap scans, citations and governed review. These are product capabilities; not customer ROI figures.
Asset and utility risk
For electric and gas utilities, with asset FMEA gap scans referencing PHMSA, NTSB and state commission evidence.
WHAT HIGHCLOUD BUILT
Engineering beyond
the AI prompt.
The useful part isn’t just generating an answer. It’s how evidence, roles and decisions connect around it.
Source-linked findings
AI-generated observations link back to public records so reviewers can examine the basis for a finding.
Human-controlled records
Engineer review and a separate risk approver govern official record changes. An AI draft is not the authoritative record.
Software-first. Workflow-aware.
Software, firmware and AI/ML failure modes are considered alongside risk signals, versioned drafts and project-scoped roles. FMEAi works with existing risk tools rather than requiring a rip-and-replace migration.
THE PRODUCT WORKFLOW
Keep the file.
Strengthen the review.
Capabilities described here reflect the public FMEAi product site. Automated monitoring is on its roadmap and is not presented as a current capability.
Bring the existing risk file
Start with the FMEA or hazard analysis your team already uses.
Surface gaps with citations
Compare the file against AI-derived failure modes and public FDA evidence.
Review, prioritize and decide
Your qualified team examines the evidence and controls the disposition of findings.
Product capabilities and design approach; not quantified customer outcomes, independent validation, FDA approval or a guarantee of regulatory compliance. FMEAi supports qualified professionals; it does not replace them. Product and mission sources: fmeai.ai and FMEAi About.
YOUR NEXT PROJECT
A real problem.
A practical next step.
Tell us what is blocking progress.
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