llms.txt
Generative AI instantly knows which services you offer, what proof you provide and how to navigate your ecosystem.
Founding framework
Interpretive governance
Your site is read by humans, search engines, generative AI and autonomous agents. Interpretive governance ensures that every reading layer understands the same thing: who you are, what you do and why it deserves trust.
Principle
What interpretive governance solves
What it is
Your site is read by four distinct layers: humans, search engines, generative AI and autonomous agents. Each layer reads differently. Interpretive governance is the discipline that ensures every layer understands the same thing: who you are, what you do, why you should be cited.
In practice, it is a set of structures deployed in your digital presence so that your organization is described correctly, cited faithfully and found by the right people.
What it is not
It is not SEO, even though SEO is part of it. It is not GEO (Generative Engine Optimization), even though comprehension by generative AI is an objective. It is not AEO (Answer Engine Optimization), even though generated answers are a concern.
Interpretive SEO covers the operational dimension. Interpretive governance covers the full framework: machine surfaces, corpus, brand, proof and consistency across all reading layers.
Why it is a commercial concern
When AI systems describe your company poorly, you lose invisible opportunities. A prospect asks an AI to compare three providers, and you are not in the answer. A partner reads a generated summary that describes you as "a digital marketing agency." These losses do not appear in any dashboard.
Interpretive debt accumulates silently. The longer it lasts, the more it costs to correct. Interpretive governance is what prevents that debt from forming, and what reduces it when it already exists.
Artefacts
How it translates concretely
Each artefact solves a specific problem: being correctly identified, accurately cited and found by the right people.
ai-manifest.json
Autonomous agents discover your public surfaces, understand your rules and interact with your presence without ambiguity.
entity-graph.json
Your identity, services and relationships stay stable in knowledge graphs: AI systems no longer confuse you with a competitor.
Programmatic JSON-LD
Every page automatically enriches search results and generated answers with your services, proof and content.
Interpretation policy
Your terms are used as-is: AI no longer swaps your vocabulary for generic synonyms that dilute your positioning.
Output constraints
Generated answers about your organization stay faithful to reality: no misleading shortcuts, no erroneous attributions.
Ecosystem
Canonical sources
The framework that protects your digital reputation rests on public, auditable and independently maintained sources.
gautierdorval.com
Doctrine and definitions
400+ articles, 44 canonical bilingual definitions. The long-form thinking that grounds the framework and feeds the execution.
Canonical definitioninterpretive-governance.org
Formal standard
The open standard (v0.3.0) that defines the rules applied to your presence: how AI should read, cite and render your content.
View the standardInferensLab.org
Research
Independent research framework. Audit frameworks, machine reading standards, experimentation.
Visit InferensLabDeployment
How we deploy it
The intervention sequence
The diagnostic identifies governance gaps. The AI governance and machine reading service deploys the artefacts. The machine-first redesign rebuilds the structure when necessary.
Ongoing governance maintains consistency over time and prevents interpretive debt from reaccumulating.
The diagnostic is the first step
It measures your current governance level, identifies gaps and recommends the right intervention sequence.
Ready to govern your interpretation?
The diagnostic measures your current governance level and identifies the gaps. It is the first concrete step.