Trustworthy AI needs independent evidence. We help you build it.
ForEvidence.ai exists to build the independent evidence capacity the AI era needs — and to make sure that capacity serves the organizations and people affected by AI, not only those selling it.
Independent by design · Standards-anchored · Built for the public interest
One mission, three ways in
Tools you can use, engagements we run with you, and a free public layer we build with partners — all anchored in evidence you can show.
The commons
A free, open layer built with nonprofits, academic centers, and foundations: the AI Oversight Workforce Index, an open assessment framework, open-source tools, and anti-deskilling research.
Join forces →The tools we build
Evidence Fabric — weaves evidence from the tools you already use into one auditable record, with nothing to install — plus Evidentia assessment infrastructure: evaluation harnesses, guardrail labs, and a public credential register.
Explore products →How we work with you
Independent AI evaluation and assurance, enterprise consultation for agentic systems and governance, and Evidentia credential programs built and run for certification bodies.
Explore services →The scarce resource in the AI era isn't models. It's trustworthy evidence.
As AI systems multiply, the hard question is no longer can we build it — it's can we trust it. That takes independent evidence: rigorous evaluation, honest assurance, and a credentialed workforce able to appraise AI claims rather than take them on faith. And as automation absorbs the entry-level work where that judgment used to form, building the oversight workforce is also how the transition stays survivable.
We are a public benefit corporation, working alongside nonprofits, academic centers, and foundations so that capacity serves the public interest — not just the vendors who profit from the technology.
How we are structuredOur commitments
- Independence from the systems we assess
- Methods anchored to recognized public standards
- Transparent, reproducible, and reviewable evidence
- Building capacity, not dependency, in every partner
Everything traces back to evidence
Whatever the engagement, our method is the same: define what "good" means in your context, gather evidence against recognized standards, and leave behind an auditable trail anyone can review. It's the spine that runs through everything we do — and it's what the name means.
- Standards-anchored: NIST AI RMF, ISO/IEC 42001, and domain-specific canons
- Reproducible evaluation harnesses and documented methodology
- Evidence packaged for regulators, boards, and the public alike
From claim to evidence
Define
Agree what "good" and "safe" mean for your context and standard.
Evaluate
Test the system with reproducible, documented methods.
Assure
Package the evidence into an auditable, reviewable record.
Ideas from the independent evidence frontier
Who certifies the people who appraise AI?
Why the demand side — not the vendors — is where the trustworthy-AI workforce must be built.
Read →What a real agent evaluation looks like
Harnesses, guardrail tests, and observability — moving beyond demos to evidence.
Read →Mapping your AI to the canon
A practical look at aligning systems to NIST AI RMF and ISO/IEC 42001.
Read →Bring evidence to your AI
Whether you're deploying agents, standing up assurance, or building a credential, we'll help you make the evidence undeniable.