Structured Data That Gets You Cited by AI (Schema for AEO)

"Use schema markup" is the most repeated and least explained advice in AEO. Everyone nods. Almost nobody tells you which types actually move the needle for getting cited by AI, or what good markup looks like.

So here's the honest version. Structured data won't force an AI engine to cite you. What it does is remove ambiguity — it tells the machine, in a language built for machines, exactly what your page says, who wrote it, and what your brand is. And when an engine is deciding which of ten similar pages to quote, the one it understands most clearly is the safer pick. That's the edge.

This guide gives you the five schema types worth your time and the copy-paste JSON-LD for each. It's the structural complement to how AI search selects citations.

Why machines need the help

An AI engine reading your page has to guess a lot: Is this a question and answer, or just prose? Who's the author — are they credible? Is this current? Is "Apple" the company or the fruit? Every guess is a chance to misread you or skip you.

Structured data answers those questions outright. It's the difference between handing someone a labelled spreadsheet and a wall of text. Both contain the facts; only one is trivial to parse correctly under time pressure — which is exactly the situation an answer engine is in.

FAQPage — the AEO workhorse

AI answers are shaped like questions and answers. FAQPage markup hands the engine your content in that exact shape — pre-chunked, pre-paired. It's the single highest-leverage type for AEO. Add it to any page that addresses common questions:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "How much does a SERP API cost?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Pricing is usually per 1,000 searches. Serpent starts at $0.05 per 1,000 quick searches on the Scale tier, with 100 free calls on signup."
      }
    },
    {
      "@type": "Question",
      "name": "What is a SERP API used for?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Developers use a SERP API to pull Google results as structured JSON for rank tracking, AI grounding, and competitor research — without scraping."
      }
    }
  ]
}
</script>

Keep the answers genuinely useful and self-contained — the way you'd want them quoted. (This very page uses the same pattern; check its source.)

HowTo — step-by-step answers

For procedural queries ("how to set up X"), HowTo gives the engine an ordered, liftable sequence:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "HowTo",
  "name": "How to track keyword rankings with an API",
  "step": [
    {"@type": "HowToStep", "position": 1, "name": "Get an API key",
     "text": "Sign up and grab your key from the dashboard."},
    {"@type": "HowToStep", "position": 2, "name": "Call the search endpoint",
     "text": "Send your keyword and country to the search endpoint."},
    {"@type": "HowToStep", "position": 3, "name": "Find your position",
     "text": "Scan the organic results array for your domain."}
  ]
}
</script>

Article — authorship & freshness

AI engines favour content they can attribute and date. Article (or BlogPosting) markup states the author, the publisher, and crucially the dateModified — freshness is a strong signal, since assistants prefer current sources:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Your article title",
  "datePublished": "2026-06-02",
  "dateModified": "2026-06-02",
  "author": {
    "@type": "Person",
    "name": "Jane Doe",
    "jobTitle": "Head of SEO",
    "url": "https://yoursite.com/about"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Your Brand",
    "logo": {"@type": "ImageObject", "url": "https://yoursite.com/logo.png"}
  }
}
</script>

Keep dateModified honest and current when you genuinely update a page — it's one of the cleaner freshness signals you control.

Organization + sameAs — entity anchoring

This is the one most teams skip, and it's quietly the most strategic. AI engines reason about entities. If the model isn't sure your brand is a real, known entity, it won't confidently recommend you. Organization markup with a sameAs array ties your site to your other authoritative profiles — turning scattered mentions into one recognized entity:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Your Brand",
  "url": "https://yoursite.com",
  "logo": "https://yoursite.com/logo.png",
  "description": "One clear sentence on what you do and who for.",
  "sameAs": [
    "https://www.linkedin.com/company/yourbrand",
    "https://x.com/yourbrand",
    "https://github.com/yourbrand",
    "https://en.wikipedia.org/wiki/Your_Brand"
  ]
}
</script>

Put this in your site-wide template (homepage at minimum). Every authoritative profile you can legitimately list in sameAs strengthens the model's confidence that you're who you say you are — which is the precondition for being recommended.

Validate, then measure

Two quick disciplines turn markup from "added" into "working":

Validate before you ship. Run every page through Google's Rich Results Test and the Schema.org validator. They catch missing required fields and syntax slips that silently break the whole block. Re-check after any template change.

Then measure the payoff. Markup is a means, not the goal — the goal is citations. Track whether the pages you've marked up start showing in AI Overviews and AI answers using the AI share-of-voice tracker, and whether they move into featured snippets. If a page is valid, fresh, and clearly an answer, yet still never cited, the gap is usually content quality or authority — not schema.

Structured data is necessary plumbing, not magic. It won't save thin content. But on strong content, it's the difference between an engine that might understand you and one that definitely does — and in a citation race, definitely wins.

FAQ

Does structured data help you get cited by AI?

Indirectly but meaningfully. It makes your content unambiguous to machines — clear Q&A, entities, and authorship — which makes you an easier, safer source to lift and cite.

Which schema types matter most for AEO?

FAQPage and HowTo (they match AI's Q&A shape), Article (authorship + freshness), and Organization with sameAs (entity anchoring). Product and Review help on commercial queries.

Is JSON-LD better than microdata?

Yes — it's a clean, separate block that's easy to maintain and parse, and it's Google's recommended format. Every example here uses it.

How do I verify my markup?

Use Google's Rich Results Test and the Schema.org validator. Validate before shipping and re-check after template changes.