Schema Markup for AI: A Step-by-Step Guide

What you'll learn:

• Why schema markup matters for AI visibility
• The 4 schema types that have the biggest impact on GEO
• Step-by-step implementation with JSON-LD code examples
• How to test and validate your schema
• Common mistakes to avoid

Why Schema Matters for AI

Schema markup adds a machine-readable layer on top of your human-readable content. AI platforms can already read your text — schema helps them understand it. When Google AI Overviews processes a page with Product schema, it knows your pricing is pricing, your features are features, and your rating is a rating. Without schema, the AI has to guess. Guessing leads to hallucinations.

Schema is one of the 7 factors AI uses to decide citations, and it's the factor with the best effort-to-impact ratio. Adding FAQ schema to a single page takes 20 minutes. The improvement in AI extractability is immediate.

FAQ Schema

FAQ schema is the single most valuable schema type for GEO. AI platforms extract FAQ content more reliably than any other format. Every page with frequently asked questions should have FAQ schema.

When to use it

Any page that answers questions — product pages, pricing pages, help pages, category pages, blog posts with FAQ sections.

JSON-LD Example

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What does your product cost?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Plans start at $49/month for Starter, $99/month for Growth, and $149/month for Pro. All plans include a 7-day free trial."
      }
    },
    {
      "@type": "Question",
      "name": "Which AI platforms do you monitor?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "We monitor ChatGPT, Perplexity, Google AI Overviews, and Claude. Each platform is audited separately with platform-specific scoring."
      }
    }
  ]
}
</script>

Organization Schema

Organization schema helps AI correctly identify your company name, description, logo, contact information, and social profiles. This directly strengthens your brand entity — one of the 7 citation factors.

When to use it

Your homepage and About page. Only add it once per page — duplicates cause confusion.

JSON-LD Example

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Your Company Name",
  "url": "https://yourcompany.com",
  "logo": "https://yourcompany.com/logo.png",
  "description": "A one-sentence description of what your company does.",
  "foundingDate": "2024",
  "sameAs": [
    "https://twitter.com/yourcompany",
    "https://linkedin.com/company/yourcompany"
  ],
  "contactPoint": {
    "@type": "ContactPoint",
    "email": "hello@yourcompany.com",
    "contactType": "customer service"
  }
}
</script>

Product Schema

Product schema is critical for reducing pricing hallucinations — the #1 type of AI inaccuracy. When AI can read your pricing from structured data instead of parsing paragraph text, accuracy improves dramatically.

When to use it

Pricing pages, product pages, and any page that describes your offering with specific attributes (price, features, ratings).

JSON-LD Example

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Growth Plan",
  "description": "AI visibility monitoring for growing brands. Includes weekly audits across all platforms.",
  "offers": {
    "@type": "Offer",
    "price": "99",
    "priceCurrency": "USD",
    "priceValidUntil": "2026-12-31",
    "availability": "https://schema.org/InStock"
  }
}
</script>

HowTo Schema

HowTo schema is ideal for guides, tutorials, and step-by-step content. AI platforms love step-by-step formats because they map cleanly to "how do I..." queries — one of the most common AI search patterns.

When to use it

Any content that walks through a process with distinct steps — setup guides, tutorials, implementation instructions.

JSON-LD Example

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "HowTo",
  "name": "How to Run Your First AI Visibility Audit",
  "step": [
    {
      "@type": "HowToStep",
      "name": "Enter your domain",
      "text": "Add your website URL and brand name to start the audit."
    },
    {
      "@type": "HowToStep",
      "name": "Select target queries",
      "text": "Choose 5-25 queries that your customers typically ask."
    },
    {
      "@type": "HowToStep",
      "name": "Review results",
      "text": "View your Visibility Score and per-platform breakdown."
    }
  ]
}
</script>

Testing Your Schema

After adding schema to your pages, validate it:

1. Google Rich Results Test — Paste your URL to confirm Google can parse your schema.
2. Schema.org Validator — Tests for structural errors in your JSON-LD.
3. Manual check — View the page source, search for "application/ld+json", and confirm the data is accurate and current.

Also read our blog post on common schema mistakes that undermine AI visibility.

Common Mistakes

• Outdated data in schema: Your schema says $79/month but your pricing page says $99/month. This makes hallucinations worse, not better.
• Duplicate schema on the same page: Two Organization schema blocks on one page confuse parsers.
• Schema without matching visible content: Google penalizes schema that doesn't reflect what users see on the page.
• Missing required properties: Each schema type has required fields. Partial schema is often ignored.

Key Takeaways

• Schema markup is the highest-impact, lowest-effort GEO improvement available.
• FAQ schema is the single most valuable type — add it to every page that answers questions.
• Product schema reduces pricing hallucinations, the most common AI inaccuracy.
• Organization schema strengthens your brand entity across all AI platforms.
• Always validate schema and keep the data current.

See if your schema is working. Run a free audit with 99Visibility to check whether AI platforms are reading your structured data correctly.