Structured Data

What Structured Data Actually Is

Structured data is a block of machine-readable markup you add to a web page that describes the page's meaning in an agreed, standardized vocabulary. The vocabulary is Schema.org, a shared dictionary of types and properties maintained collaboratively by Google, Microsoft, Yahoo, and Yandex. The delivery format that all of them recommend is JSON-LD, short for JavaScript Object Notation for Linked Data: a small script, sitting quietly in your page inside a <script type="application/ld+json"> tag, that carries the facts without touching a single pixel of what a human sees.

A concrete example makes the idea click. A human reading a plumber's homepage understands, from context and layout, that "Reliable Rooter" is a business, that the phone number in the footer belongs to it, and that the five gold stars near the top are a customer rating. A machine sees only a stream of text and tags, and has to guess at all of it. Structured data removes the guessing by declaring it outright:

The most useful types for a business are a small, learnable set. Organization and LocalBusiness establish the entity itself and its contact and location facts. Product and Service describe what you sell. AggregateRating and Review carry proof. FAQPage marks up genuine question-and-answer content. Each type nests inside the others, so a LocalBusiness can contain its own address, opening hours, and rating as properties, building a single coherent statement of fact rather than a scatter of loose signals. This explicit, typed way of stating who you are is closely related to semantic HTML, which does the same job for the structure of your visible content.

Why It Decides Whether AI Can Find and Cite You

Retrieval systems, whether a classic search index or an AI answer engine, reward certainty. When a model must infer a fact, it assigns that inference a confidence, and low-confidence facts are the first thing dropped when an answer has to be short, accurate, and defensible. Structured data raises the confidence to near-certainty, because the fact is no longer a guess drawn from surrounding prose: it is a declaration in a format the machine was built to trust. This is the essence of retrieval legibility, whether your facts are stated in a form a machine can read without ambiguity.

That certainty flows into two places that matter enormously. The first is the knowledge graph, the vast entity database that engines like Google, and increasingly the large language models behind AI assistants, use to understand who and what exists in the world. Well-formed Organization and LocalBusiness markup helps an engine connect your website to a stable entity in that graph, so the system knows your business is a real, resolved thing rather than an unmatched string of characters. The second is the set of enriched surfaces built on top of that trust: rich results in search, and the direct answers and recommendations produced by AI. A model asked to recommend a Denver plumber leans hard on explicitly stated, corroborated facts. Ambiguity is friction, and friction loses the citation.

How to Use It Well, and Honestly

The strategy that follows from all of this is disciplined rather than clever. Mark up the entities and facts that are actually true and actually on your page, using the most specific type that fits: a dentist should be a Dentist, not just a generic LocalBusiness, because specificity gives the engine more to resolve. Keep the markup and the visible page in agreement, because engines cross-check the two, and structured data that claims a rating or a price the page does not show is treated as spam and can earn a penalty rather than a reward.

How AIOInsights Reads This Signal

Structured data sits at the heart of our AI Discoverability pillar, because it is the most direct way to make your facts legible to a machine. AIOInsights parses the JSON-LD on your page and checks concrete, observable conditions: whether an Organization or LocalBusiness entity is declared at all, whether the type is specific rather than generic, whether the core properties like name, address, and telephone are present and complete, and whether the markup validates against the Schema.org shape rather than throwing errors. It also cross-references the markup against your visible content, because claims that the page does not back up are a liability, not an asset. You can see exactly how this feeds the overall score in our scoring methodology.

Every one of those checks is real and deterministic. We do not estimate or invent a number. We read the actual markup in your page, evaluate it against fixed, documented criteria, and report what is there and what is missing, so the same site always earns the same score for the same reasons. Structured data closely supports what llms.txt does at the site level: both make your truth easy for a machine to find and hard for it to misread.

Check Your Structured Data

Keep reading the lexicon: Knowledge Graph, Retrieval Legibility, and llms.txt.