CertEdge Report
A pre-market verification service that runs a med-tech company's quantized edge AI through fault-injection and sensitivity simulations, then outputs the safety-case evidence pack for FDA / CE submissions.
QA and regulatory affairs leads at medical AI device startups
- Automated bit-flip injection sweeps across the deployed quantized model
- Per-bit criticality map showing which weights most affect diagnostic decisions
- Side-by-side report comparing static quantization vs adaptive precision
- Exportable evidence binder formatted for IEC 62304 and FDA 510(k) review
Regulators are tightening expectations for AI in safety-critical devices, and embedded ML teams lack tooling that turns arithmetic-level fault analysis into submission-ready documentation.
FDA AI-enabled device list is large and growing with new 2026 guidance tightening expectations, but the subset of med-tech startups running quantized edge AI specifically is a narrow slice; mixed real but concentrated demand.FDA Issues Comprehensive Draft Guidance for Developers of Artificial Intelligence-Enabled Medical Devices ↗FDA 2026 AI Medical Device Guidance: Key Updates ↗
Edge Case Research (ecr.ai) already sells a DevSafeOps platform doing safety-case authoring, hazard analysis, UL 4600, and regulatory readiness for autonomy/medical — this is a direct, funded incumbent, not whitespace.Data-Driven Safety Tools & Operations | DevSafeOps | Edge Case ↗Safety Intelligence Using AI for Continuous Compliance | Edge Case ↗
Regulatory submission work commands premium pricing and med-tech startups do pay for FDA/CE services, but the addressable buyers are few and incumbents like ECR already capture spend; pricing not publicly disclosed.Edge Case Research - Products, Competitors, Financials, Employees ↗
FDA, Health Canada, and EU AI Act are all ratcheting requirements for AI in safety-critical devices over multi-year horizons — this is a durable, regulatorily mandated tailwind.FDA AI/ML Medical Device Compliance Guide 2026 — GLACIS ↗
Fault-injection and failure-analysis toolchains exist in academia (surveys, model-implemented FI platforms, microprocessor FI frameworks), and regulatory templates are known, but bridging arithmetic-level quantization analysis to submission-ready docs requires deep dual expertise in embedded ML and FDA/CE practice.A Survey on Failure Analysis and Fault Injection in AI Systems ↗Real-time Embedded System Fault Injector Framework for Micro ... ↗