Engagement decision
How to recognize that this axis should be mobilized
Use this page as a decision page. The objective is not only to understand the concept, but to identify the symptoms, framing errors, use cases, and surfaces to open in order to correct the right problem.
Typical symptoms
- Visibility is discussed as one score although the outputs vary by system and prompt class.
- The brand is present but its role, limits or evidence are unstable.
- The team wants to improve AI visibility before knowing which sources govern the answer.
- Dashboards identify appearances without identifying the canon-output gap.
Frequent framing errors
- Treating the audit label as a promise of ranking, citation, recommendation or model compliance.
- Optimizing the visible symptom before identifying the governing canonical surface.
- Confusing market-facing vocabulary with the stricter doctrine of answer legitimacy and proof of fidelity.
- Producing screenshots or scores without preserving prompts, sources, answer traces and correction routes.
Use cases
- Qualifying a market-facing AI visibility symptom before opening a full governance intervention.
- Separating presence, citation, framing, recommendation, source authority and answer fidelity.
- Prioritizing which canonical pages, expertise pages, governance files or proof artifacts must be reinforced.
- Converting AI-search observations into auditable correction work.
What gets corrected concretely
- Define the canonical surface that should govern the answer.
- Map cited, structuring and governing sources separately.
- Record the prompt, output, sources, claim class, gap and recommended correction.
- Route the issue toward proof of fidelity, representation gap, source hierarchy, semantic architecture or interpretive governance.
Relevant machine-first artifacts
These surfaces bound the problem before detailed correction begins.
Governance files to open first
Useful evidence surfaces
These surfaces connect diagnosis, observation, fidelity, and audit.
References to open first
Governance artifacts
Governance files brought into scope by this page
This page is anchored to published surfaces that declare identity, precedence, limits, and the corpus reading conditions. Their order below gives the recommended reading sequence.
Definitions canon
/canon.md
Canonical surface that fixes identity, roles, negations, and divergence rules.
- Governs
- Public identity, roles, and attributes that must not drift.
- Bounds
- Extrapolations, entity collisions, and abusive requalification.
Does not guarantee: A canonical surface reduces ambiguity; it does not guarantee faithful restitution on its own.
Site context
/site-context.md
Notice that qualifies the nature of the site, its reference function, and its non-transactional limits.
- Governs
- Editorial framing, temporality, and the readability of explicit changes.
- Bounds
- Silent drifts and readings that assume stability without checking versions.
Does not guarantee: Versioning makes a gap auditable; it does not automatically correct outputs already in circulation.
Public AI manifest
/ai-manifest.json
Structured inventory of the surfaces, registries, and modules that extend the canonical entrypoint.
- Governs
- Access order across surfaces and initial precedence.
- Bounds
- Free readings that bypass the canon or the published order.
Does not guarantee: This surface publishes a reading order; it does not force execution or obedience.
Complementary artifacts (3)
These surfaces extend the main block. They add context, discovery, routing, or observation depending on the topic.
Canonical AI entrypoint
/.well-known/ai-governance.json
Neutral entrypoint that declares the governance map, precedence chain, and the surfaces to read first.
Q-Ledger JSON
/.well-known/q-ledger.json
Machine-first journal of observations, baselines, and versioned gaps.
Q-Metrics JSON
/.well-known/q-metrics.json
Descriptive metrics surface for observing gaps, snapshots, and comparisons.
Evidence layer
Probative surfaces brought into scope by this page
This page does more than point to governance files. It is also anchored to surfaces that make observation, traceability, fidelity, and audit more reconstructible. Their order below makes the minimal evidence chain explicit.
- 01Canon and scopeDefinitions canon
- 02Weak observationQ-Ledger
- 03Derived measurementQ-Metrics
- 04External contextCitations
Definitions canon
/canon.md
Opposable base for identity, scope, roles, and negations that must survive synthesis.
- Makes provable
- The reference corpus against which fidelity can be evaluated.
- Does not prove
- Neither that a system already consults it nor that an observed response stays faithful to it.
- Use when
- Before any observation, test, audit, or correction.
Q-Ledger
/.well-known/q-ledger.json
Public ledger of inferred sessions that makes some observed consultations and sequences visible.
- Makes provable
- That a behavior was observed as weak, dated, contextualized trace evidence.
- Does not prove
- Neither actor identity, system obedience, nor strong proof of activation.
- Use when
- When it is necessary to distinguish descriptive observation from strong attestation.
Q-Metrics
/.well-known/q-metrics.json
Derived layer that makes some variations more comparable from one snapshot to another.
- Makes provable
- That an observed signal can be compared, versioned, and challenged as a descriptive indicator.
- Does not prove
- Neither the truth of a representation, the fidelity of an output, nor real steering on its own.
- Use when
- To compare windows, prioritize an audit, and document a before/after.
Citations
/citations.md
Minimal external reference surface used to contextualize some concepts without delegating canonical authority to them.
- Makes provable
- That an external reference can be cited as explicit context rather than silently inferred.
- Does not prove
- Neither endorsement, neutrality, nor the fidelity of a final answer.
- Use when
- When a page uses external sources, sector references, or vocabulary anchors.
AI visibility audit
AI visibility audit is a service-facing market bridge for teams that describe their problem as AI visibility, AI search, ChatGPT visibility, citation tracking, brand representation or GEO before the deeper governance issue has been qualified.
It is built around this working definition: a market-facing audit that separates AI presence, citation, framing, recommendation, answer inclusion and representation stability across AI-mediated search and answer systems.
Canonical definition: AI visibility audit.
What this page captures
This page does not present a packaged service, a fixed offer, a price, a performance guarantee or a promise of ranking. It captures a recurring market entry point and routes it toward a governed diagnostic process.
The practical intent is general market bridge for organizations that say “AI visibility” before the more precise problem has been qualified.
The page therefore acts as a bridge between market language and the stricter doctrine of interpretive governance, representation gap audit, AI search monitoring, AI citation analysis, AI source mapping and proof of fidelity.
Search demand captured
This page intentionally captures demand around phrases such as:
AI visibility auditAI search visibilityAI brand visibilityvisibility in AI search
Those phrases are useful because they describe how buyers and teams formulate the problem. They are dangerous when treated as the whole problem. The same search phrase can hide very different situations: absence, weak citability, wrong category, source substitution, comparison drift, unsupported recommendation, or an answer that sounds coherent without being governed.
Typical symptoms
- Visibility is discussed as one score although the outputs vary by system and prompt class.
- The brand is present but its role, limits or evidence are unstable.
- The team wants to improve AI visibility before knowing which sources govern the answer.
- Dashboards identify appearances without identifying the canon-output gap.
Diagnostic route
A serious AI visibility audit should follow five checks.
- Presence check: where does the entity, brand, page or concept appear, disappear or vary?
- Citation check: which sources are cited, and are they merely displayed or actually structuring the answer?
- Representation check: does the answer preserve the canonical role, scope, exclusions, services and limits?
- Authority check: which source should govern the claim, and does the answer respect the source hierarchy?
- Correction check: which canonical page, artifact, link, definition, category, expertise page or external echo must be corrected or reinforced?
The output should never be only a dashboard. It should become an auditable route.
What the audit must not promise
This audit label must not be read as a promise of ranking, citation, recommendation, traffic, ChatGPT inclusion, model compliance, correction speed or cross-system stability. AI-mediated systems vary by model, prompt, source access, retrieval state and answer policy. The goal is to improve the governability of representation, not to claim direct control over third-party systems.
Evidence expected
A credible audit should preserve:
- the prompt class and exact prompts used;
- the system, date, answer and visible citations;
- the claim class being evaluated;
- the canonical surface that should govern the answer;
- the detected gap between canon and output;
- the source hierarchy or authority conflict;
- the recommended correction route;
- whether the issue belongs to visibility, citability, recommendation, representation, source mapping, semantic architecture or interpretive governance.
This evidence layer connects the market-facing audit to interpretive observability, interpretive auditability, Q-Ledger and Q-Metrics.
Related service axes
- AI Search Monitoring
- AI citation analysis
- AI source mapping
- Representation gap audit
- Comparative audits
- Drift detection
- Pre-launch semantic analysis
- Interpretive risk assessment
- Independent reporting
Related canonical concepts
- LLM visibility
- Citability
- Recommendability
- AI brand representation
- AI answer audit
- Canon-output gap
- Proof of fidelity
- Answer legitimacy
Phase 13 rule
Market labels are admissible as entry points, not as governing concepts. A AI visibility audit becomes useful only when it routes demand toward canon, source hierarchy, evidence, proof of fidelity, response legitimacy and correction discipline.
Phase 14 service-intent note
This page is the primary service or audit entry point for AI visibility audit. Definition pages explain terms; this page owns diagnostic, advisory, and audit intent.
Global routing: SERP ownership map.
Request route
To turn this expertise page into a concrete request, use the contact page with the target entity, relevant URLs, AI systems observed, sample outputs, and decision context. Those elements make it possible to separate a visibility issue from a representation, evidence, authority, or correction issue.