AI search doesn't read your page the way a person does — it extracts facts and synthesizes an answer. Schema markup is how you hand those systems clean, unambiguous facts about your business, so AI Overviews, ChatGPT, and Perplexity can understand you and quote you accurately instead of guessing or skipping you.
Large language models are probabilistic — they predict, and prediction allows for error. Structured data narrows that error. When your page explicitly labels "this is the business name," "this is the rating," "this is the author and their expertise," an AI system can lift those facts with far higher confidence. That's why structured data feeds the knowledge graphs these platforms lean on, and why clean schema reduces the odds of being misquoted or left out of an answer entirely.
For AI visibility, the goal isn't rich-result eligibility — it's being a clearly defined entity. That means nailing the types that establish who you are and what you know, then linking them to references the models already trust.
Make your site AI-legible →sameAs links to the profiles that corroborate it.@id and sameAsTies you to Wikipedia, Wikidata, and the Google Knowledge Graph.Anyone promising guaranteed AI citations is selling something. AI systems weigh authority, relevance, and corroboration across the whole web — schema is one strong input, not the only one. What structured data reliably does is remove ambiguity: it makes your facts machine-certain and your entity well-defined, which is the prerequisite for being pulled into an answer. Pair it with genuine expertise and consistent signals across your profiles, and you've done the part that's actually in your control. And the same rule still applies — never mark up anything that isn't visible on the page.
I build entity-first schema — clean JSON-LD, an @graph that links your entities, and the knowledge-graph signals AI leans on. Let's make your site easy to understand and safe to cite.