Structured data for AI search

Structured data for AI search means the same Schema.org vocabulary that powers Google rich results, usually written as JSON-LD in a script tag, applied with AI answer engines in mind. Common types include Organization, Product, FAQPage, Article and Person. The markup labels facts on the page, such as who published it and what a product costs, so machines do not have to infer them from prose.

The clearest gains are on entities. Organization markup with sameAs links to your Wikipedia article, LinkedIn page and Crunchbase profile helps knowledge graphs merge scattered mentions into one record of your company, and answer engines draw on those graphs when they ground brand facts. Product markup carries price, availability and review data into the search indexes that Google AI Mode and Bing-backed assistants query. FAQPage markup helps mostly through its shape: question and answer pairs chunk cleanly for retrieval.

The limit is that large language models mostly read rendered text, not the markup layer beneath it. When GPTBot or ClaudeBot fetches a page, the pipeline extracts visible copy; a JSON-LD block rarely decides what gets quoted in an answer. Schema will not compensate for vague positioning or a pricing page that contradicts your homepage, because the model quotes the prose it saw. Treat markup as hygiene that removes ambiguity rather than as a ranking lever for AI answers.

A sensible order of work: add Organization markup with accurate sameAs links sitewide, add Product or Service markup to commercial pages, and keep every field consistent with the visible text, since mismatches can cost you the rich results that still feed these systems. Then check the output side. Discoverable's free AI visibility checker shows how assistants currently describe your brand, with no login needed for the snapshot, and that description is the real test of whether your marked-up facts reached the model.