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.
Bridge vocabulary
/bridge-vocabulary.json
Registry of market terms and their routes toward appropriate concepts, clarifications and boundaries.
- Governs
- Lexical translation between branding, reputation, visibility and interpretive governance.
- Bounds
- Automatic canonization of market terms and unsupported inferences.
Does not guarantee: Lexical routing creates neither evidence, model control nor favorable reputation.
serp-ownership.json
/serp-ownership.json
Published machine-first governance surface.
- Governs
- Part of the corpus reading conditions.
- Bounds
- An inference zone that would otherwise remain implicit.
Does not guarantee: This file does not, on its own, guarantee system obedience.
Semantic router
/semantic-router.json
Surface that orients reading toward the right parts of the corpus by intent type.
- Governs
- Discoverability, crawl orientation, and the mapping of published surfaces.
- Bounds
- Incomplete readings that ignore structure, routes, or the preferred markdown surface.
Does not guarantee: A good discovery surface improves access; it is not sufficient on its own to govern reconstruction.
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.
- 01Evidence artifactfamily-proof-requirements.json
- 02Evidence artifactsource-weighting-policy.json
family-proof-requirements.json
/family-proof-requirements.json
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.
source-weighting-policy.json
/source-weighting-policy.json
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.
Why ChatGPT describes your company incorrectly: eight causes to separate before correcting
When an answer describes a company incorrectly, teams often diagnose hallucination or insufficient content. Sometimes they are right, but effective correction begins by identifying the failure mode.
More pages will not fix a namesake. A slogan change will not repair an obsolete third-party source. Positive tone will not correct false scope.
Eight causes
- Entity confusion: company, product, subsidiary, group or namesake are merged. Correct identifiers, relations and exclusions.
- Overly broad category: a specialist is absorbed into a common generic category. Strengthen specialization evidence and problem architecture.
- Historical identity dominance: former name, offer or position remains more salient. Use chronology, redirects, dates and source consolidation.
- Inverted hierarchy: true secondary facts become central. Rework hubs, internal relations and structuring evidence.
- Unbounded scope: the answer adds a service, audience, territory or promise. Publish scope, exclusions and non-implications.
- Structuring third-party source: an external page governs category or claim. Identify its competence, update when possible and preserve legitimate criticism.
- Prompt or context mismatch: language, region, browsing, session memory or intent changes the result. Use comparable protocol and baseline.
- Factual hallucination: price, certification, executive or event is invented. Preserve the claim, correct the competent source, report where possible and re-observe.
Decision tree
| Question | If yes | If no |
|---|---|---|
| Does the claim belong to the correct entity? | Continue | Entity confusion |
| Is the fact supported? | Examine framing | Hallucination or weak source |
| Is the primary category correct? | Examine scope | Category drift |
| Is the state current? | Examine relations | Temporal drift |
| Are boundaries preserved? | Examine sources | Scope drift |
| Does the gap repeat? | Escalate diagnosis | Monitor incident |
What a defensible correction contains
Evidence before correction, competent source or layer, bounded change, expected hypothesis, re-observation protocol, stopping criteria and no promise about future model behaviour.
Do not use the wrong profession’s tool
A branding symptom may be an entity error. An SEO symptom may be external authority. A reputation symptom may be accurate criticism. A monitoring symptom may be protocol drift.
The first skill is not optimization. It is problem disambiguation. The AI brand representation audit performs that separation before recommending action.