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 artifactbridge-vocabulary.json
- 02Evidence artifactconcept-registry.json
- 03Evidence artifactserp-ownership.json
- 04Evidence artifactfamily-proof-requirements.json
bridge-vocabulary.json
/bridge-vocabulary.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.
concept-registry.json
/concept-registry.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.
serp-ownership.json
/serp-ownership.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.
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.
AI brand perception
AI brand perception is a market term for the way a generative system presents a brand: which attributes it selects, which category it assigns, which competitors it associates, what tone it uses and why it cites or recommends the brand.
The term is useful as an entry point, but a model does not perceive a brand as a person does. It produces an output from context, retrieved material, parameters, instructions and learned relations. What can be observed is therefore not a psychological state. It is a generated representation under documented conditions.
Working definition
AI brand perception is the set of attributes, categories, relations, evaluations and frames observable in generated answers about a brand for a defined protocol of prompts, models, languages, regions and dates.
A single answer does not establish “the perception of AI.” It is one observation. A family of observations may reveal a pattern, instability or drift when conditions are recorded.
Observable dimensions
| Dimension | Question | Main risk |
|---|---|---|
| Identity | Is the correct entity named? | Confusion or fusion |
| Category | In which market is the brand placed? | Generalization or displacement |
| Scope | Which services are treated as central? | Outdated or badly ranked offer |
| Differentiation | Which attributes distinguish it? | Dilution |
| Relations | Which competitors or partners are associated? | Wrong neighbourhood |
| Time | Which version of the brand is described? | Historical inertia |
| Sentiment | Is the tone favourable, neutral or critical? | Tone mistaken for fidelity |
| Recommendability | For which needs is the brand proposed? | Wrong use case |
What the term cannot establish
Positive generated perception does not prove accurate representation. A system can sound favourable while placing the company in the wrong category. A negative answer is not automatically drift; it may reflect qualified external evidence or legitimate criticism.
The term also does not measure human brand perception. Establishing public image or reputation requires external methods such as research, behavioural data, surveys, media, reviews and history. AI answers may reproduce or influence those signals, but they do not replace them.
Variation is not automatically drift. It may result from prompt wording, language, region, browsing mode, model updates or different retrieval. Drift requires a baseline and a comparable protocol.
Relation to AI brand representation
AI brand representation is the canonical concept for the portrait produced. AI brand perception is the market language used for that portrait and its variation. AI perception drift describes observable change across time or conditions.
The correct route is:
market term → observed output → representation → comparison with canon → qualified gap.
Evidence condition
A claim about how a system presents a brand should preserve the prompt, output, model or product, date, language, known region, browsing state and cited sources. A pattern requires several observations. Drift requires a comparable reference state.
AI brand perception is therefore a useful audit entry point, not a self-sufficient score. Its value comes from separating presence, sentiment, category, source and fidelity.