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
- 04Evidence artifactsite-context.md
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.
site-context.md
/site-context.md
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
Market visibility, citability, and recommendability
This lexical family consolidates the terms that the market uses when it talks about visibility inside AI-mediated search systems: LLM visibility, citability, recommendability, monitoring, GEO, brand representation, and AI answer audits.
The purpose of this family is not to replace the stricter interpretive governance canon. It creates a bridge from search demand to governable concepts. Each term is accepted as a public entry point, then routed toward source hierarchy, proof of fidelity, interpretive observability, machine readability, and answer legitimacy.
Canonical terms
- LLM visibility
- Citability
- Recommendability
- AI search monitoring
- GEO metrics
- AI citation tracking
- AI brand representation
- Brand visibility in ChatGPT
- Generative engine optimization
- AI search optimization
- AI answer audit
- Semantic integrity
- Semantic accountability
- Delegated meaning
Reading order
Start with LLM visibility, then distinguish citability from recommendability. Use AI search monitoring and AI citation tracking as observation layers. Use GEO metrics only as measurement, not as proof. Then connect the observed problem to AI brand representation, AI answer audit, and the evidence layer.
Why this family matters
Most organizations will not begin by asking for interpretive governance. They will ask why they are absent from ChatGPT, why competitors appear, why their brand is not cited, why a dashboard reports visibility without explanation, or why an AI answer describes them incorrectly.
This family captures those queries without letting them flatten the doctrine. It makes clear that visibility, citation, recommendation, monitoring, and optimization are not the same state. They are separate thresholds that require different evidence and different interventions.
Canonical routing rule
Use this family as a bridge from market vocabulary to governing vocabulary. When a term here is used, route the diagnosis toward canonical source, source hierarchy, proof of fidelity, interpretive observability, Q-Metrics, and answer legitimacy.
Phase 13 routing layer: service audits and market entry points
Phase 13 adds a service-facing routing layer for audit demand: LLM visibility audit, AI answer audit, AI brand representation audit, representation gap audit, AI citation analysis, AI source mapping, comparative audits, drift detection, pre-launch semantic analysis, interpretive risk assessment, and independent reporting.
These terms should be treated as market entry points. They capture real demand, then route the work toward canon, source hierarchy, evidence, answer legitimacy, auditability, and correction resorption.
How to read this lexical family
This family translates doctrine into market-facing language. LLM visibility, citability, recommendability, AI search monitoring and GEO metrics are terms that organizations already use or will understand quickly. They are useful entry points, but they are not sufficient governance categories.
The family should be read as a bridge. Visibility asks whether the entity appears. Citability asks whether it is used as a source. Recommendability asks whether it is selected as an option. Representation asks whether it is described correctly. Interpretive governance asks whether these outputs are legitimate, bounded and faithful.
Typical misreadings
The main error is to treat visibility as success. A brand can be visible for the wrong reason, cited in the wrong context, recommended under the wrong category or described through a distorted framing. More mentions can worsen the problem when the representation is not governed.
A second error is to treat GEO metrics as the whole field. Metrics can show symptoms, but they do not by themselves explain source hierarchy, answer legitimacy, semantic contamination, proof of fidelity or correction strategy.
Use in audit and routing
Use this family as the bridge between market demand and doctrinal diagnosis. It is appropriate for prospects who ask about ChatGPT visibility, AI search, citations, recommendations, brand representation or GEO. The page should then route them toward the deeper concepts required to interpret the problem.
For SERP routing, this family supports service pages, audit pages and market hubs. It should not replace the canonical definitions that govern proof, authority, source hierarchy and interpretive risk.