Evidence-first audit FAQ

The audit checks documented AI actors such as GPTBot, ClaudeBot, and PerplexityBot, plus other documented AI actors in the selected audit profile. Each actor is audited separately, with its own robots-policy reading, so a policy decision that treats one actor differently does not get collapsed into another.

The free tier is 5 scans per month, no credit card required. PDF reports and the sealed evidence bundle are downloadable on every tier, including free. Starter ($99/mo) and Growth ($299/mo) are on the map for higher volume — contact us and you will be in the first wave when purchase opens.

It runs deterministic, reproducible execution audits against your live URLs and produces a sealed evidence bundle of observed behavior. A rescan then verifies whether a named remediation actually landed. It does not promise rankings, citations, or any model behavior — it reports what your site serves, with evidence.

Yes. The audit evaluates what a public URL actually serves — HTTP fetch, robots policy, raw and rendered HTML, structured data — so it works with any stack: WordPress, Next.js, Webflow, static sites, or a custom CMS.

It records whether a direct public HTTP probe can acquire the submitted page, evaluates how its robots policy addresses named AI consumers, and compares raw and browser-rendered extraction under a versioned protocol set. Results include evidence-linked findings, concrete remediations, explicit limitations, and finding-specific rescan verification.

No. The core product evaluates acquisition, robots policy, raw and rendered representations, deterministic main-content extraction, structured data, and date evidence. It does not treat a provider mention or citation count as the audit result.

Not in the evidence-first result. A universal score would collapse different actors, purposes, protocol failures, and unknowns into false precision. The core result is PASS, FAIL, or INCOMPLETE, supported by the protocol record. The experimental research layer can also produce a secondary comparison index; it is clearly labeled, display-only, and never an input to the verdict.

PASS means every required protocol in the named audit profile completed and no evidence-linked production blocker remains open. It does not mean that a provider will crawl, index, rank, cite, recommend, train on, or understand the page.

FAIL means the audit observed at least one production blocker. INCOMPLETE means required evidence was not successfully obtained, so compatibility was not established. Missing evidence is never converted into a pass.

Not automatically. Discovery, training, grounding, and user-action actors have different purposes and policy semantics. The selected audit profile states which actors are required, informational, or excluded. An intentional training opt-out is not mislabeled as a discovery defect.

A chatbot can help with writing or interpretation. It does not replace recorded HTTP evidence, actor-specific policy evaluation, distinct raw and rendered artifacts, deterministic protocol execution, immutable evidence hashes, or remediation-specific verification.

The baseline remediation names the protocols and expected outcome required to clear it. The later scan re-runs those protocols against the same target and compatible audit profile. The result is VERIFIED, NOT VERIFIED, or INDETERMINATE. Finding counts and score movement are not used as proof.

No. Experimental observations are visibly separated and cannot create a production blocker or determine the verdict.