Schema Markup for AI Search: Organization & FAQ JSON-LD (with Examples)

Schema markup (JSON-LD structured data) tells search and AI systems *what your content means* in a machine-readable format. For AI visibility, two types do most of the work: Organization (who you are) and FAQPage (quotable Q&A the model can lift).

1. Organization schema

Put this in the <head> of your homepage. It resolves your business as an entity.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Your Business",
  "url": "https://yoursite.com",
  "logo": "https://yoursite.com/logo.png",
  "description": "What you do, for whom, and the outcome.",
  "sameAs": [
    "https://www.linkedin.com/company/yourbusiness",
    "https://twitter.com/yourbusiness"
  ]
}
</script>

Use LocalBusiness instead of Organization (with address and openingHours) if you serve a local area.

2. FAQ schema

Add this on pages with real questions. Models love lifting clean Q&A.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "What does Your Business do?",
    "acceptedAnswer": { "@type": "Answer", "text": "Plain, quotable one-paragraph answer." }
  }, {
    "@type": "Question",
    "name": "How much does it cost?",
    "acceptedAnswer": { "@type": "Answer", "text": "Pricing starts at $X/mo." }
  }]
}
</script>

Rules that keep it working

The schema must match the visible content on the page (don't fake it).
Keep answers short and quotable — one clear paragraph each.
Validate with a structured-data testing tool before publishing.
Pair schema with llms.txt and third-party citations — schema alone won't make you recommended, but its absence holds you back.

Why this matters for AI

Structured data lowers the model's uncertainty about who you are and what you claim. Lower uncertainty = higher odds it represents and recommends you correctly.

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