Structured data for AI is machine-readable page facts, usually JSON-LD, that state plainly what a page is and what it claims, so an engine can quote you precisely instead of guessing from prose. It is the difference between hoping a crawler infers a fact and handing it the fact.
Why it matters
An engine reading your page has to work out what it is: a product, an organization, an answer to a question. Prose leaves that to inference, and inference is where facts get mangled. Structured data states it outright, in a format built to be read by machines.
It is not a ranking trick. It is a contract: here is my name, here is what this product does, here are the questions this page answers. An engine that trusts the source can lift those facts cleanly instead of paraphrasing and drifting.
The honesty catch is that structured data must match the page. Marking up a claim the page does not make, or a review that did not happen, is exactly the kind of thing engines are learning to punish.
How Jinn treats it
Jinn emits the structured data it recommends. Its own pages carry organization, product, and defined-term markup built from fact-checked inputs, with no marked-up question that lacks a real answer and no social profile that is not genuinely ours.
The rule is we emit what we sell. Structured data is treated as a crawler-facing contract, tested for shape and guarded against stale positioning, because a fact stated for a machine has to be one we would stand behind to a person.
Related terms
Hand the engine the fact, do not make it guess.
Structured data is a contract a crawler can quote. See how Jinn builds a record engines can read.