evidlab
When regulators, investors, or enterprise buyers ask whether your AI is trustworthy and ready for regulatory scrutiny, internal testing is rarely enough. We provide independent, credible evidence to support enterprise adoption, investor due diligence, and regulatory review.
Apply for free pilot with findings! No spam. Ever.
From model access to evidence dossier in three steps.
Secure API access or on-prem deployment. Your data never leaves your infrastructure.
Multi-turn patient conversations, adversarial safety probes, and realistic messy inputs — scored against physician-validated scenarios built on named clinical guidelines.
A findings report ranks every failure with the exact transcripts and quotes that triggered it. You can fix the issues and we'll re-run for free!
A citable report mapped to your framework — FDA, US state AI laws, RUAIH, NIST, EU AI Act, MDR, and more — with methods, results, and limitations, ready for due diligence, procurement, or regulators.
Every model is measured the same way, every time. Our protocols are versioned, published, and designed to survive scrutiny.
Clinical validity — missed red flags, unsafe advice, and care-level routing, scored against physician-validated scenarios
Safety & hallucination — crisis protocol adherence, scope boundaries, and adversarial probing for dangerous advice
Robustness — real-world phrasing: typos, dialects, atypical presentations, consistency across repeated runs
Compliance behaviors — deterministic checks for AI disclosure, crisis resources, and required disclaimers
Illustrative. One measured behavior, mapped to every framework in scope — acceptance thresholds set per engagement, every status traceable to a dossier section.
Evidence is only useful if regulators, buyers, and boards trust how it was produced.
We use de-identified test data, restrict access to each evaluation, and sign Business Associate Agreements when required.
Reports structured to support USA (FDA, US state-laws, etc.) and EU (AI Act, MDR) technical documentation.
We never build or fine-tune the models we evaluate. No equity, no consulting conflicts — just evidence.