Structured data for AI

Models parse your markup, not your design. Structured data tells a machine exactly what your content means, so it can trust you, quote you correctly, and connect you to the right entity. This is the unglamorous foundation under everything else.

Ambiguity is what gets you skipped

A model reading raw HTML has to infer what everything means. Is that number a price, a rating, or a phone number? Is that name the author or a quoted source? Every inference is a chance to get it wrong. Structured data removes the guessing by labeling your facts in a format built for machines.

In classic search, schema was a nice-to-have that earned you rich snippets. In AI search, it is closer to a requirement, because a model deciding whether to cite you is weighing how confident it can be in what you mean. Clear markup raises that confidence. It is the base layer of agent legibility, beneath llms.txt and the action protocols.

The schema types that earn their keep

TypeWhat it declaresBest for
OrganizationWho you are, plus sameAs links to your profilesEvery site, as the entity anchor
Product / OfferItems, prices, availabilityEcommerce
ServiceWhat you do and whereService businesses
FAQPage / HowToQuestions, answers, and stepsGuidance content and AEO
Review / AggregateRatingProof and reputationProducts and services
SpeakableSpecificationThe parts best read aloudVoice and assistant answers

Grab copy-paste versions of these from our free schema templates. The point is not to mark up everything, it is to mark up the facts you want machines to repeat.

Entities, sameAs, and the knowledge graph

Beneath the markup is a bigger idea. Search and AI systems do not think in keywords, they think in entities: real things with names, attributes, and relationships, stored in a knowledge graph. Your job is to make your entity unmistakable. That means an Organization schema with sameAs links to every profile you control, consistent naming everywhere, and facts that match across your site and the wider web.

When you do this well, a machine stops guessing whether the WebPossible on your site is the WebPossible on LinkedIn is the WebPossible mentioned in an article. It knows. That certainty is the quiet thing that decides citations. We go deeper in entities and the knowledge graph and rich results.

Structured data for AI, answered

Why does structured data matter for AI search?
Structured data, usually JSON-LD schema markup, states the meaning of your content in a format a machine does not have to interpret. It tells a model that this string is a price, this is a rating, this is the author, this is a step. That removes ambiguity, which lowers the chance of being misread or skipped, and raises the chance of being cited accurately. For AI search it is foundational, not optional.
Which schema types matter most?
Start with Organization (with sameAs links to your profiles), then add the types that fit your content: Product and Offer for ecommerce, Service for service businesses, FAQPage and HowTo for guidance, Article for content, Review and AggregateRating for proof, and BreadcrumbList for structure. SpeakableSpecification marks the parts best suited to being read aloud by an assistant.
What is an entity and why does it matter?
An entity is a thing the web agrees exists: your company, a person, a product, a place. Search and AI systems maintain a knowledge graph of entities and their relationships. When your schema, your sameAs links, and your mentions across the web all point to the same well-defined entity, machines can identify you confidently. That confidence is what gets you cited.
Does schema help with rich results too?
Yes. The same structured data that helps AI understand you also powers rich results in classic search, such as review stars, FAQ accordions, and product details. So the work pays off in both the old search world and the new one, which makes it one of the highest-return investments you can make.

Get the schema templates

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