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 artifactfalse-neighbors.json
- 02Evidence artifactsemantic-proximity-separation.json
- 03Evidence artifactcommon-misinterpretations.json
false-neighbors.json
/false-neighbors.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.
semantic-proximity-separation.json
/semantic-proximity-separation.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.
common-misinterpretations.json
/common-misinterpretations.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.
Brand identity, brand image and AI representation
These terms all describe a portrait of the brand, but they differ in origin, authority and evidence.
| Object | Mainly produced by | Question | Suitable evidence |
|---|---|---|---|
| Brand identity | The organization | Who are we, what do we do and what boundaries do we declare? | Canonical sources, offer, policy and entity architecture |
| Brand image | Audiences and market | What associations form around the brand? | Research, behaviour, media, surveys and qualified reviews |
| AI representation | A system in context | What portrait does this answer produce now? | Prompt, output, model, date, sources and protocol |
Identity has declarative, not universal, authority
The brand is competent to declare its name, status, current offer, audience, intended position and exclusions. That authority does not make every evaluative claim true. Leadership, excellence and reputation require other evidence classes.
Image cannot be read directly from one answer
AI output can reproduce market associations and may help spread new ones. It is not a representative audience sample. “The system presents the brand as premium” is an output observation. “The brand has a premium image” requires external evidence.
Representation is reconstructed
AI representation may combine official identity, third-party descriptions, history, comparisons, citations and inference. It can preserve, reduce, extend or distort identity. An audit must separate canon, external evidence and system synthesis.
Example
A firm declares a specialist identity in industrial cybersecurity. Customers regard it as highly technical and reliable. An AI answer calls it a general IT provider. Identity, image and representation diverge. The divergence does not automatically establish which is “right”; it identifies where sources, category and reception must be checked.
Governance rule
Do not use official material to certify external image. Do not use one AI output as a direct measure of public opinion. Do not use desired image as factual canon.
The correct sequence is: identify the object → select competent sources → observe the output → compare without fusion.