Add Schema Markup
TL;DR
Schema markup tells AI systems what your content means, not just what it says. Adding JSON-LD structured data for your organization, articles, products, and FAQs makes your site dramatically easier for AI to parse and cite.
Last updated: 2026-03-09
Why Schema Matters for AI#
When an AI system reads your page, it sees text. It does not automatically know whether that text describes a product, an article, a person, or a business. Schema markup closes that gap. It wraps your content in machine-readable labels that tell AI exactly what everything is.
Think of schema as name tags for your content. Without them, AI has to guess. With them, it knows instantly that "$49.99" is a price, "John Smith" is an author, and "Acme Corp" is an organization. That precision matters because AI systems prioritize sources they can parse cleanly and accurately.
Sites with strong schema markup consistently score higher in AgentReady™ scans. More importantly, they are more likely to be cited by ChatGPT, Perplexity, and Google AI Overviews because the AI can extract structured facts with confidence. If your site lacks schema, you are leaving citations on the table.
Quick-Start with JSON-LD#
JSON-LD (JavaScript Object Notation for Linked Data) is the recommended format for schema markup. Google, Bing, and all major AI systems support it. Unlike older formats like Microdata, JSON-LD sits in a single
<script> tag in your page's <head> section. It does not touch your HTML structure at all, which makes it easy to add without breaking your design.
Every JSON-LD block starts with a @context declaration pointing to schema.org and a @type that identifies what the data describes. From there, you fill in the properties relevant to that type. The example below shows the basic structure you will use for all schema markup on your site.Basic JSON-LD structure for an Organization
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Your Company Name",
"url": "https://www.example.com",
"logo": "https://www.example.com/logo.png",
"description": "A brief description of your organization.",
"sameAs": [
"https://twitter.com/yourcompany",
"https://www.linkedin.com/company/yourcompany"
]
}
</script>html
Adding Organization Schema#
Organization schema is the foundation. It tells AI who you are, what you do, and where to find your official profiles. Every website should have Organization schema on at least the homepage.
Start with the required fields:
name, url, and description. Then add logo, sameAs (an array of your social profile URLs), contactPoint for customer service details, and foundingDate if relevant. The more complete your Organization schema is, the stronger your authority signals become.
Place this in the <head> of your homepage. If you have a multi-brand or multi-location business, you can use SubOrganization or LocalBusiness types on specific pages. The key is consistency: AI systems cross-reference your Organization schema with what they find on your social profiles, Wikipedia, and other sources to build entity confidence.Complete Organization schema example
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Acme Corp",
"url": "https://www.acmecorp.com",
"logo": "https://www.acmecorp.com/images/logo.png",
"description": "Acme Corp provides enterprise project management tools for mid-market companies.",
"foundingDate": "2015-03-01",
"sameAs": [
"https://twitter.com/acmecorp",
"https://www.linkedin.com/company/acmecorp",
"https://github.com/acmecorp"
],
"contactPoint": {
"@type": "ContactPoint",
"telephone": "+1-800-555-0199",
"contactType": "customer service",
"availableLanguage": ["English"]
}
}json
Adding Article and Product Schema#
Beyond Organization schema, two types give you the biggest AI readiness lift: Article and Product.
Article schema tells AI that a page is editorial content. Include the
headline, author (as a Person with a name and url), datePublished, dateModified, and publisher. This is critical for blog posts, guides, and news content. AI systems use these fields to assess freshness and authorship, which feed directly into content quality signals.
Product schema applies to any page selling or describing a product. Include name, description, brand, offers (with price, priceCurrency, and availability), and aggregateRating if you have reviews. Product schema helps AI accurately quote your pricing and availability in shopping-related queries.
For pages that answer common questions, add FAQPage schema. Each question-answer pair becomes a mainEntity entry. AI systems prefer FAQ schema because it gives them pre-structured answers they can cite directly.Article schema with author and publisher
{
"@context": "https://schema.org",
"@type": "Article",
"headline": "How to Improve Your AI Readiness Score",
"author": {
"@type": "Person",
"name": "Jane Doe",
"url": "https://www.example.com/team/jane-doe"
},
"datePublished": "2026-02-15",
"dateModified": "2026-03-01",
"publisher": {
"@type": "Organization",
"name": "Example Inc",
"logo": {
"@type": "ImageObject",
"url": "https://www.example.com/logo.png"
}
},
"description": "A practical guide to improving your site's AI readiness score with schema markup, content structure, and protocol support."
}json
Validating Your Markup#
Adding schema is only half the job. You need to validate it. Broken or invalid schema is worse than no schema because it can confuse AI systems and search engines.
Use Google's Rich Results Test (search.google.com/test/rich-results) to check individual pages. Paste your URL or your raw JSON-LD code and the tool will flag errors and warnings. Fix all errors before deploying. Warnings are optional but worth addressing.
For a broader view, use Google Search Console's Enhancement reports. They show schema issues across your entire site, not just one page. Schema.org's own validator (validator.schema.org) is also useful for checking that your types and properties are correct.
After deploying, run an AgentReady™ scan to see how your schema changes affect your overall AI readiness score. Schema improvements typically show up in your schema markup factor score immediately.
Common Mistakes to Avoid#
The most frequent mistake is adding schema that does not match the visible page content. If your Article schema says the author is "Jane Doe" but no author byline appears on the page, AI systems may flag a mismatch and reduce trust. Schema must reflect reality.
Other common errors include using deprecated properties, nesting types incorrectly, omitting required fields, and duplicating schema blocks. If you have two Organization schema blocks on the same page with different names, AI does not know which one to trust.
Avoid generating schema with tools and never reviewing the output. Automated generators often produce incomplete or generic markup. Always review and customize the output for your specific content.
- Schema content must match visible page content exactly
- Never duplicate the same schema type on a single page
- Always include required fields for each type (check schema.org for the list)
- Keep dateModified current — stale dates signal neglected content
- Test after every change using Rich Results Test
Related Pages
Frequently Asked Questions
Where should I put JSON-LD on my page?
Place your JSON-LD script tag inside the <head> section of your HTML. It works in the <body> too, but <head> is the standard practice and ensures crawlers encounter it first.
Can I have multiple JSON-LD blocks on one page?
Yes, you can have multiple JSON-LD blocks as long as each describes a different entity. For example, a blog post page might have both Article schema and Organization schema. Just avoid duplicating the same type with conflicting data.
Do I need schema on every page or just the homepage?
Organization schema belongs on the homepage. Article schema goes on every blog post or editorial page. Product schema goes on every product page. The goal is to describe every important page in machine-readable terms. Start with your highest-traffic pages and expand from there.
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