Schema Markup for AI Search: Which Types Matter and How to Use Them

Structured data is machine-readable metadata that explicitly tells AI systems what your content is, who created it, and what it contains. Without it, AI systems must infer your content type and entity identity from unstructured text, a process that is less reliable and less complete.

For AI citation specifically, structured data serves two functions: it establishes entity identity (who you are) and it marks extractable content (what you know). Both are required for consistent AI citation.

Organization schema is the anchor of your AI entity identity. It tells AI systems your brand name, URL, logo, description, contact information, and social profiles. Critically, the @id property creates a persistent identifier for your organization that other schema types can reference.

The sameAs property is particularly important. It should list your verified social profiles and any authoritative external pages (Crunchbase, LinkedIn company page, Wikipedia if applicable). These create the entity association web that AI knowledge graphs use.

FAQPage schema is the most direct path to AI answer extraction. It explicitly marks question-answer pairs that AI systems can retrieve and surface. A page with well-implemented FAQPage schema is essentially presenting a pre-extracted Q&A index to AI systems.

FAQPage schema should only be added to pages where the FAQ content is visibly rendered. Adding it to pages without matching visible content creates a mismatch that search engines flag as an error.

All structured data should be implemented as JSON-LD: a script tag with type="application/ld+json" in the page head. JSON-LD is preferred over microdata and RDFa because it is separated from HTML, easier to maintain, and supported by all major AI and search systems.

In Next.js App Router, add JSON-LD to server components using a script tag with dangerouslySetInnerHTML. This ensures the schema is present in the server-rendered HTML, not injected client-side.