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.
Canonical AI entrypoint
/.well-known/ai-governance.json
Neutral entrypoint that declares the governance map, precedence chain, and the surfaces to read first.
- 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.
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.
Definitions canon
/canon.md
Canonical surface that fixes identity, roles, negations, and divergence rules.
- Governs
- Public identity, roles, and attributes that must not drift.
- Bounds
- Extrapolations, entity collisions, and abusive requalification.
Does not guarantee: A canonical surface reduces ambiguity; it does not guarantee faithful restitution on its own.
Complementary artifacts (3)
These surfaces extend the main block. They add context, discovery, routing, or observation depending on the topic.
Identity lock
/identity.json
Identity file that bounds critical attributes and reduces biographical or professional collisions.
Dual Web index
/dualweb-index.md
Canonical index of published surfaces, precedence, and extended machine-first reading.
LLMs.txt
/llms.txt
Short discovery surface that points systems toward the useful machine-first entry surfaces.
Machine-first visibility operating model
This framework describes an operational sequence for building the conditions of early AI visibility without waiting for a site to accumulate strong organic inertia.
It is not a universal recipe. It is an execution order designed to reduce the delay between publication, machine understanding, and emergence in responses.
Guiding principle
The guiding principle is the following: visibility should not be treated at the end of the chain; it should be built into the interpretable architecture of the site.
The work therefore consists in producing an environment that can be:
- properly crawled;
- cleanly indexed;
- understood without excessive extrapolation;
- synthesized without scope collapse;
- recommended without relying only on long organic history.
Operational sequence
MV-1: full technical cleanup
Objective: prevent technical issues from sabotaging legibility.
Top priorities:
- rendering and HTML accessibility;
- HTTP status, canonicals, redirects, sitemaps;
- minimally coherent internal linking;
- absence of major duplication;
- stability of URLs and publication paths.
Without that base, semantic and doctrinal layers rest on unstable ground.
MV-2: clarification of the central object
Objective: make the site immediately understandable as a system.
Four questions must be answered explicitly:
- who publishes;
- what is being published;
- for what scope;
- what is not covered.
That implies entity pages, definitions, exclusions, and a clear separation between doctrine, offer, proof, and experimentation.
MV-3: structuring the internal graph
Objective: replace a simple link network with a graph of meaning.
The following must be organized:
- hubs;
- relationships between concepts;
- categories;
- internal authority hierarchies;
- links between definitions, doctrine, frameworks, articles, and proofs.
A site that points without hierarchy may remain findable, but it will remain weakly governable.
MV-4: documentary densification
Objective: reduce the share of free inference.
The priority surfaces to publish are:
- canonical definitions;
- doctrinal pages;
- frameworks;
- FAQs;
- comparisons;
- use cases;
- case studies and field observations.
The more explicit surfaces a site provides, the less systems are forced to fill the gaps.
MV-5: machine-first surfaces and governance
Objective: make machine reading more direct and more coherent.
Depending on context, this may include:
- machine-first indexes;
- governance files;
- entity graphs;
- manifests;
- routing conventions;
- declarative rules for scope, authority, and exclusion.
Those surfaces have value only when they faithfully extend the editorial canon.
MV-6: cross-surface alignment
Objective: avoid telling one thing in one place and another thing elsewhere.
Alignment must be checked between:
- homepage;
- pillar pages;
- entity pages;
- definitions;
- doctrine;
- machine-first documentation;
- structured metadata.
Early machine visibility often emerges from a rare cross-surface coherence.
MV-7: multi-system validation
Objective: observe whether the site is actually being mobilized as intended.
One then tests:
- how the site is formulated across several systems;
- the stability of scopes;
- which objects are actually retained;
- merger, projection, or extension errors;
- the queries for which visibility appears early.
This step is not for self-congratulation. It is for correcting what remains too freely interpretable.
MV-8: secondary organic consolidation
Objective: turn emergence into a defensible position.
Once early AI visibility is observed, one must continue working on:
- external authority;
- links;
- distribution;
- reputation;
- public proof;
- publication continuity.
The framework does not oppose machine-first and organic. It sequences them intelligently.
Discipline rules
- Rule 1: never publish a machine-first surface that contradicts the human canon.
- Rule 2: do not confuse documentary density with textual inflation.
- Rule 3: do not treat governance as decorative varnish.
- Rule 4: connect every new piece of content to an already stabilized node.
- Rule 5: date and contextualize every observed visibility proof.
Expected positive symptoms
When the framework begins to produce effects, one may observe:
- a sharper understanding of positioning;
- stronger convergence of descriptions across systems;
- faster emergence on specialized queries;
- fewer generic or vague reformulations;
- reduced out-of-scope projection.
Limits
This framework guarantees neither organic dominance, nor systematic citation, nor automatic conversion. It describes a site production mode that improves the chances of obtaining faster and cleaner AI visibility.