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.
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.
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
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.
AI-mediated answer systems may select sources from adjacent subqueries, not only from the visible user query.
This is why fan-out behavior matters. A user asks one question, but the system may internally ask several. It may need definitions, comparisons, constraints, recent data, examples, risks, products, locations or source confirmation before producing the final answer.
A page optimized only for the visible query can therefore be weaker than a source environment that covers the whole retrieval cluster.
What a fan-out query is
A fan-out query is a secondary, adjacent or decomposed query generated from an initial user request in order to retrieve enough evidence to construct an answer.
The visible query is the starting point. The fan-out set is the actual retrieval environment.
Example
A user asks: “What is the best way to prepare a B2B site for AI citations?”
The system may implicitly look for:
- AI citation factors;
- AI visibility audits;
- LLM visibility;
- answer-engine optimization;
- structured data and AI search;
- source hierarchy;
- AI crawler access;
- citation tracking;
- brand representation in AI answers;
- examples of citable passages.
If a site covers only “AI citation factors” but not the adjacent concepts, it may lose retrieval opportunities.
Why this changes SEO strategy
Traditional SEO often begins with a primary keyword and expands into related terms. AI-mediated retrieval requires a stricter map: what does the system need to know before it can produce a legitimate answer?
This shifts the unit of optimization from page to cluster, and from cluster to source environment.
The best content does not merely match a query. It provides the pieces that make the answer reconstructable: definition, mechanism, scope, evidence, limits and route to deeper authority.
The fan-out failure modes
| Failure mode | Consequence |
|---|---|
| Single-keyword content | The source appears on one query but disappears from the retrieval cluster |
| Weak adjacent definitions | The system uses a competitor or generic source to fill missing context |
| No source hierarchy | The system cannot identify which page should govern the answer |
| Poor internal linking | The system finds isolated fragments instead of a stable corpus |
| Unclear entity relations | The system confuses service, doctrine, framework and brand positioning |
How to build for fan-out retrieval
A fan-out-ready cluster should contain:
- a hub page that names the topic and its boundaries;
- definitions for the concepts the answer will need;
- service pages for applied intent;
- doctrinal pages for authority and scope;
- practical frameworks for operational use;
- internal links that expose the intended route;
- passages that can be cited without losing context.
The goal is not to flood the site with thin pages. The goal is to create a corpus where the system can resolve the user’s subquestions without substituting weak external sources.
Governance implication
Fan-out retrieval creates a source selection problem. If the site does not declare which sources should govern which claims, an answer system may combine fragments in a plausible but illegitimate way.
This is where AI citation readiness moves beyond search optimization. The source environment must not only be found. It must be governable.