LLMO: make your website readable by AI
Here, LLMO is a framework for technical audits: crawlers, metadata, structured data and entity consistency. It improves website quality without guaranteeing indexing or citations by a platform.
Technical checks you can actually act on
llms.txt & llms-full.txt
Optional community formats for presenting links and content in Markdown. They are neither an official standard nor a condition for visibility.
Accurate structured data
Schema.org is used when it accurately describes the visible content. Google requires no special markup for its AI features.
Linked entities (knowledge graph)
Link official profiles that actually exist. A Wikidata entry is considered only if its rules and criteria are met.
Controlled LLM crawlability
Document the crawlers listed by each platform and choose a policy based on their role. Allowing a crawler guarantees neither indexing nor citations.
Structure content as a knowledge graph
Three documented layers, no magic file
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Access
An explicit policy for crawlers and their documented roles.
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Description
Metadata and structured data that accurately reflect the page.
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Consistency
Connected authors, organisation and official profiles.
Platforms do not all disclose the same processes, and some combine multiple sources. It is therefore unwise to infer their behaviour from a single notion of 'ingestion'. We distinguish documented facts from what remains an assumption.
The work covers three layers: technical access based on the crawlers documented by platforms, content description using accurate metadata, and entity consistency between the website and its official profiles.
An llms.txt file can be tested as a complementary format, but its effect must remain an assumption. The concrete gains come from cleaning up the website: accessible pages, accurate information, identifiable authors, official links and a deliberate robots.txt policy.
Verifiable elements, without an artificial score
Concrete deliverables
Optional llms.txt file: a structured website description, priority pages and explicit acknowledgement of its status as a community proposal
Semantic schema audit: identifying missing schemas and markup errors, with validation using Schema.org Validator and Rich Results Test
Schema.org revision limited to relevant types and properties confirmed by the visible content
Entity strategy: consistency across official profiles, authors and verifiable sameAs links
robots.txt management: separate policies for search crawlers, training crawlers and user-initiated requests
Accessible content: a clear hierarchy, self-contained passages, and lists and tables only where they improve understanding
Factual metadata: title, description, author, publication date and update date where these are accurate
Monitoring: server logs and documented tests, without equating a crawler visit with indexing or a citation
4 steps, measurable deliverables at each
LLM technical audit
Checks of robots.txt, server logs, existing semantic schemas, markup errors and accessibility for LLM user agents.
llms.txt & schemas
Structured data clean-up and, where useful, creation of an llms.txt file clearly presented as experimental.
Entity graph
Verification of official profiles and sameAs links. Wikidata is used only if the entity meets its own criteria.
Monitoring & policy
Monitoring observed access and adjusting the policy according to the website owner's objectives and platform documentation.
LLMO FAQ
LLMO vs GEO: what is the difference?
What exactly is an llms.txt file?
Should you allow or block LLM crawlers?
Does Schema.org markup influence LLMs?
Wikidata: why is it key to LLMO?
Can AI read your website?
LLMO audit: robots.txt policy, structured data, entities and technical observations, with a clear distinction between facts and assumptions.
