Schema markup is structured data that hands engines your facts in a format they do not have to guess about. For AI answers it is disproportionately valuable, because a model composing a recommendation prefers facts it can verify mechanically.
The schema types that move AI answers
- LocalBusiness / ProfessionalService: who you are, where you operate, how to reach you. The backbone of every local recommendation.
- Service: each service as its own typed entity, not a bullet in prose.
- FAQPage: literal question-and-answer pairs, the exact shape AI answers are made of.
- Review / AggregateRating: engines love a number they can compare.
- Organization with sameAs: links your site to your profiles, collapsing duplicate identities into one entity.
The mistakes that cost citations
Schema that contradicts the page. If your markup says one service area and your footer says another, you have manufactured distrust. Orphan schema, markup describing things the page never mentions, gets discounted. Missing sameAs leaves engines unsure whether your Google profile and your website are the same business, and unsure engines hedge you out of answers.
How to verify it is working
Validate with Google's Rich Results Test, then do the test that actually matters: ask the engines about your business and watch whether descriptions tighten over the following weeks. Correct schema shows up as confidence, engines stating your services and area plainly instead of hedging.
Not sure what your site currently exposes to the machines? That is the first page of our free AI visibility audit.
Does schema guarantee AI citations?
No single signal guarantees anything. Schema removes ambiguity, which raises the probability an engine states your facts as facts rather than hedging or omitting you.
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