Certifications check the paperwork. We measure the model.

RUAIH made AI governance a certification question.

Certification pathways opened in June 2026, and governance reviews check that policies and oversight exist. A quality committee eventually asks a different question out loud: how does the model itself behave on our question types?

The instrument works from the model's internal signals while your real clinical and operational question types run against an endpoint you designate. What comes back is a map of where the model fabricates and how often, with each flagged answer quoted in full so your clinicians can judge it themselves. The deliverable is a signed, dated screening measurement for the governance file. Detection performance so far: 0.852 AUC on models the instrument had never seen, fabricated-entity discrimination, length-controlled, leave-one-model-out. Method paper DOI: 10.5281/zenodo.21365654.

No PHI, ever, and nothing installed.

Spectralgraph is a no-PHI vendor by design. The test needs representative question types and a model endpoint you designate, never patient data, so there is no BAA to negotiate. In scope: a self-hosted open-weight model, a vLLM-class private-cloud deployment, or an internal fine-tune your system controls. Private Azure OpenAI exposes probabilities on output tokens only, which isn't enough for the instrument to read. Epic-embedded and vendor-scribe AI that the vendor attests to is out of scope.

For tribal health systems the same posture serves data sovereignty. The measurement comes to your designated endpoint, and your data goes nowhere.

What an engagement costs.

The LLM Validation Report is $9,500. Continuous Assurance is $6,500 per quarter for one model. The pre-deployment Model Selection Study is $4,500 for two candidates, up to $7,500 for four. Full terms are on the pricing page. No work begins without a signed engagement letter, and no fee is ever success-based.

Questions health compliance teams ask first