Definition
What is Structured Data for AI?
Structured Data for AI extends traditional schema markup to serve the needs of AI engines alongside search engines. While Schema.org markup has long been used for SEO (helping Google understand page content for rich snippets), its importance has increased with AI engines that rely on structured signals to comprehend content context, relationships, and authority.
The structured data ecosystem for AI includes several layers. Schema.org markup (JSON-LD format) provides content-level structure: Article, Product, FAQ, HowTo, DefinedTerm, Organization, and other types that help AI engines understand what each page contains. AI-specific files (llms.txt, llm-profile.json, .well-known/ai.txt) provide brand-level structure: who you are, what you do, and how you should be described.
For AI Overviews and Gemini, structured data directly influences whether your content is selected for citation. Google's AI systems use schema markup to understand content type, authorship, publication date, and topical relevance. Pages with rich structured data are more likely to be included in AI-generated summaries.
Implementing structured data for AI is a Technical AEO task that provides compounding benefits. Once in place, it enhances how AI engines process all of your content—not just the pages where the markup is implemented. It signals to AI systems that your site takes machine-readability seriously, potentially increasing trust and citation frequency.
Why it matters
Structured data provides the machine-readable context that AI engines need to accurately understand, categorise, and cite your content. Without it, AI engines must infer context from unstructured text, increasing the risk of misinterpretation or omission. Implementing structured data is one of the highest-ROI Technical AEO activities.
Real-world examples
- 1
Adding DefinedTerm schema to glossary pages so AI engines understand they contain authoritative definitions
- 2
Implementing FAQ schema that enables direct extraction by ChatGPT and AI Overviews
- 3
Creating llm-profile.json with structured brand information for AI crawlers
Frequently asked questions about Structured Data for AI
Explore related concepts
Technical AEO
technicalTechnical AEO encompasses the infrastructure and technical configurations that help AI engines discover, crawl, parse, and cite your content. It includes AI-specific crawl policies, structured data implementation, llms.txt files, site architecture optimisation, and content formatting for AI consumption.
llms.txt
technicalllms.txt is a plain-text file placed at a website's root that provides structured, machine-readable information about a brand, product, or organisation specifically for consumption by large language models. It functions as a "robots.txt for AI" — telling AI crawlers what your brand is and how it should be described.
llm-profile.json
technicalllm-profile.json is a JSON-LD structured data file placed at .well-known/llm-profile.json that provides machine-readable brand identity, offerings, expertise, and preferred citation formats to AI crawlers and language models.
Content for AI
strategyContent for AI refers to the practice of creating and structuring website content specifically to be effectively consumed, understood, and cited by AI engines. It involves answer-first formatting, clear factual claims, structured data, and comprehensive coverage of topics.
Start with the pages and proof that AI can actually use
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