Getting cited
Does schema markup help with AI search visibility?
What schema actually buys you
Its real value is entity resolution. Linking your brand to its authoritative records with sameAs tells an engine that the name on your page is the same organisation it knows from its knowledge graph, rather than a similarly named one.
The types that earn their keep are Organization with sameAs, FAQPage and QAPage for question content, Product with real aggregate ratings, Dataset for original data, and Article with clear dates and authorship.
Where it does nothing
Schema does not make weak content citable. Marking up a page that answers nothing produces a well-described page that answers nothing.
It also cannot contradict the visible page. Structured data that claims ratings or prices not shown on the page is a policy violation in traditional search and does nothing useful in AI search.
See where you actually stand
We ask AI engines the questions your buyers ask and show you whether you were named, who was named instead, and which sources they cited. Free.
Related questions
Entity SEO is making sure search and AI systems understand your brand as a specific, identifiable thing rather than a string of characters. It matters because AI engines reconcile what they read against a knowledge graph — a brand they cannot resolve confidently is one they are reluctant to name.
Barely, on current evidence. Around 9–10% of major sites publish an llms.txt, but monitoring of AI crawler traffic shows the file is almost never requested — GPTBot, ClaudeBot, PerplexityBot and Google-Extended overwhelmingly crawl HTML directly. No major AI company has committed to reading it, and Google has said it does not support it.
Pages that answer one question directly, near the top, under a heading that matches how the question is asked. Comparison tables, specification lists, dated statistics, and short definitional passages are extracted most often, because each can be lifted whole and remain true without the surrounding page.
More on getting cited
Last reviewed 2026-07-30.