AI Visibility Glossary

Schema Markup

2026-07-09 · Definition · by Italo Campilii
Definition

Schema markup is structured code, usually written in JSON-LD using the Schema.org vocabulary, added to a webpage to describe its content explicitly to search engines and AI systems, enabling rich results and helping machines understand entities, relationships, and meaning.

By labeling elements such as products, articles, events, or FAQs with standardized types and properties, schema markup removes ambiguity about what a page contains. This can unlock rich results in search, star ratings, prices, event dates, and gives crawlers a machine-readable summary that supplements the visible HTML.

For AI answer engines, schema acts as a reliable interpretation layer. Acromatico implements precise Schema.org markup so language models can extract facts, attributes, and entity relationships without guessing. Clear structured data increases the odds that a brand's information is retrieved accurately and cited correctly across AI-generated responses.

Related terms

Questions people ask

What is the difference between schema markup and structured data?

Structured data is the general concept of organizing information in a machine-readable format. Schema markup is the specific implementation using the Schema.org vocabulary, typically in JSON-LD. In practice the terms overlap heavily, but schema markup refers to that particular shared vocabulary.

Does schema markup guarantee rich results?

No. Valid schema makes a page eligible for rich results, but search engines decide whether to display them based on quality, relevance, and their own criteria. Correct markup improves your chances and machine understanding, yet it never guarantees a specific enhanced appearance in results.

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