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What is Structured Data?
Structured data (schema markup) is standardized code, typically Schema.org vocabulary in JSON-LD, added to a webpage so search engines and AI systems can understand its content unambiguously.
Structured data is a way of describing a webpage's content in a format built for machines rather than humans. A visitor reads a page and infers, from layout and context, that a block of text is a business address, a customer review, or an answer to a specific question. A crawler or language model can't reliably make that same inference from raw HTML alone, headings, paragraphs, and divs don't carry meaning on their own. Structured data closes that gap by explicitly labeling content using a shared vocabulary, most commonly Schema.org, embedded in a page as JSON-LD.
Common schema types include LocalBusiness (name, address, phone, hours), FAQPage (question-and-answer pairs), HowTo (step-by-step instructions), Review and AggregateRating, and Article. Each one tells a search engine or AI system exactly what a given piece of content is, not "here's some text that looks like a question," but "this specific text is the question, and this specific text is its answer." That precision is what lets Google render rich results like star ratings or FAQ dropdowns directly in search results, and it's increasingly what lets AI engines extract a clean answer with confidence instead of having to guess.
Structured data matters more, not less, in an AI search context. A model deciding whether to cite a page is working under a tight budget and has to resolve ambiguity fast; unambiguous markup removes the guesswork and makes a page a safer, faster citation. It's also foundational to entity clarity: schema is how a business tells search engines and AI models exactly who it is, which matters both for local search and for being represented accurately across the wider web (see GEO).
Ritam Labs' audit checks structured data as part of both SEO On-Page (D2) and AEO/GEO Readiness (D3), since the same markup that helps traditional rich results also helps AI extraction, and Local SEO (D6), where LocalBusiness schema specifically is checked alongside NAP consistency. Missing or malformed schema is one of the most common, and most mechanically fixable, findings a fix generator resolves.
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