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GLOSSARY · TECHNICAL

Schema markup for AI

Schema markup for AI is structured data, usually schema.org vocabulary written as JSON-LD, that states facts about a page explicitly (who the company is, what a product costs, who wrote an article) so AI systems do not have to infer them from prose. It reduces misreading; it does not guarantee a mention or a citation.

Which types matter most

  • Organization: legal and brand name, logo, website, and sameAs links to your official profiles. This is the backbone of a consistent brand identity.
  • Product with Offer: name, brand, price, currency, availability. Add ratings only if they are real, on-page reviews.
  • Article: headline, author, publish and update dates. Dates and authorship are trust signals.
  • BreadcrumbList: where the page sits on the site.
  • FAQPage: only for questions and answers that are visible on the page. Never mark up hidden text.

Example (Organization)

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Example Skincare",
  "url": "https://www.example.com",
  "logo": "https://www.example.com/logo.png",
  "sameAs": [
    "https://www.linkedin.com/company/example-skincare",
    "https://www.instagram.com/exampleskincare"
  ]
}

What schema does not do

No published controlled study shows that a particular schema type reliably increases AI citations, so treat claims of fixed percentage lifts with care. Schema cannot fix a page an engine cannot fetch, facts that contradict the rest of the web, or content that does not answer the question. Markup that disagrees with what the page shows can hurt trust.

How ClerAEO measures it

The free scan checks whether a site publishes structured data that engines can read, as part of its AI-readiness checks.

Frequently asked questions

Do I need schema markup to appear in ChatGPT answers?

No. Many brands appear without it. Schema helps engines read your facts correctly once they find your page; it does not make the page findable on its own.

Related terms

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