Automated AI consumers are not one system, and website compatibility is not one score.
Audit
Record what each named protocol observed.
Diagnose
Trace each production finding to evidence.
Verify
Re-run the protocol required by the remediation.
A page can look correct in a browser while a direct client receives an access denial, an empty application shell, an unresolved redirect, malformed structured data, or a policy rule that applies differently to different actors.
Those are concrete compatibility conditions. They can be observed, repaired, and re-tested — which makes them auditable. Accountability starts with evidence a CFO can read and a client can inspect, not an abstract readiness grade.
Generative Metrics audits acquisition, actor policy, representation, extraction, supported structured data, and strict date evidence. It links production findings to those observations and verifies remediations on later scans.
What it delivers instead is accountability: verification proof — whatever the outcome — for every remediation, and a white-label report agencies can deliver under their own brand.
It is not a trust oracle, an SEO guarantee, or a prediction that a provider will crawl, index, train on, rank, cite, recommend, or understand a page.
Acquisition artifacts and protocol observations are recorded before findings or remediation are derived.
Discovery, training, grounding, and user-action consumers are not treated as one generic AI bot.
Failed or insufficient evidence produces FAILED, ABSTAINED, INCOMPLETE, or INDETERMINATE—not a fabricated score.
Every remediation names the protocol and expected outcome a later scan must satisfy.
Individual checks exist. The hard part is preserving the acquisition artifact, applying the correct actor profile, separating representations, carrying limitations forward, preventing experimental output from becoming production truth, and proving whether a named remediation actually cleared.
The product is the controlled evidence chain and the repeatable workflow—not ownership of HTTP, robots.txt, JSON-LD, or content extraction as isolated techniques.
Run a bounded audit, inspect the evidence and limitations, fix one production blocker, and verify that remediation under the same protocol set.
