BE DISCOVERABLE BY AI
robots.txt, sitemaps, canonicals, indexability and AI crawler access form the discovery foundation.
A market term for making web information easier for large language model and retrieval systems to resolve, understand and use correctly.
Google does not define LLMO as a separate Google Search discipline. Its official AI Search guidance emphasizes accessible text, crawlability, unique useful content, internal links and matching structured data.
Clear entities, stable canonical URLs, semantic HTML, internal links and coherent sections reduce ambiguity for retrieval and language-model workflows.
The practical goal is not to teach an LLM your site; it is to remove ambiguity that makes your offer harder to retrieve or interpret.
HTML&HTML uses entity integrity, semantic structure, internal-link alignment, canonical consistency, language consistency and knowledge-surface checks.
LLMO is not an official Google ranking factor or a guarantee that a model will mention a brand.
HTML&HTML prepares your website for visibility, citation eligibility and recommendation opportunity across AI search experiences. It shows measurable website-side blockers that can prevent discovery, understanding and source consideration.
robots.txt, sitemaps, canonicals, indexability and AI crawler access form the discovery foundation.
GEO, AEO, LLMO, entity graphs, schema and answer extractability reduce machine ambiguity.
RAG/retrieval, original information, E-E-A-T, freshness and evidence support source eligibility.
AAO, accessible journeys, intact links, measurable referrals and clear CTAs connect AI discovery to commercial action.
Recommendations, rankings, citations, traffic, customers and revenue are not guaranteed. HTML&HTML measures website-side technical and content blockers; it does not claim control over external AI systems.