Structured Data for AI
Structured data for AI is the use of machine-readable markup — schema.org JSON-LD for organizations, products, FAQs, and offers — to state facts about your business unambiguously. While LLMs primarily read prose, the retrieval and parsing layers of answer engines use structured data to disambiguate entities and extract facts, making markup a low-effort GEO foundation.
Structured data's role in the AI stack is often misunderstood. The language model itself does not 'read JSON-LD' at inference time — but the systems that feed it do: crawlers and retrieval pipelines use markup to identify entities, associate facts with the right subject, and select which page answers which question. Markup makes your pages easier to use correctly.
The schema types with the highest leverage for B2B SaaS map to the facts buyers verify: Organization (identity), Product/SoftwareApplication (what it is), Offer (pricing), FAQPage (extractable answers), and DefinedTerm for glossary content. Each states a checkable fact in a form that cannot be misparsed.
Treat markup as a claims registry: whatever facts you need AI to get right — price, plan limits, compliance, integrations — should exist in both readable prose and structured form, and be updated in the same release that changes the fact.
Related terms
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