Schema Markup for AI Search: Audit, Fix, Get Cited
Pages with 3+ schema types are 2.8x more likely to be cited by AI. Our audit checks what's missing and gives you the JSON-LD to paste.
Why Schema Matters for AI Citation
Schema markup is structured data that tells AI engines exactly what your page is about, who wrote it, and when it was updated. For AI search — ChatGPT, Claude, Perplexity, Google AI Mode — schema is the difference between content AI can verify and content AI has to guess about.
Schema used to be "nice to have" for rich snippets. In 2026, it's how AI verifies your content's credibility.
What the Schema Branch Checks
Presence Checks
- •Article schema (headline, datePublished, dateModified, author)
- •Person schema (name, credentials, jobTitle)
- •Organization schema (name, url, logo)
- •FAQPage schema (question/answer pairs)
- •Product schema
- •Breadcrumb schema
- •Review/Rating schema
Quality Checks
- •JSON-LD syntax validity
- •Schema matches visible content
- •Entity graph completeness (linked via @id)
- •dateModified freshness
- •Required properties present
The Entity Graph — Why Connected Schema Matters
A single schema block is useful. Connected schema blocks are powerful. When your Article, Person, and Organization schemas reference each other via @id, you create an entity graph — a verifiable relationship AI can follow.
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "Person",
"@id": "https://example.com/#author-jane",
"name": "Jane Smith",
"jobTitle": "Senior SEO Strategist",
"worksFor": { "@id": "https://example.com/#org" }
},
{
"@type": "Organization",
"@id": "https://example.com/#org",
"name": "Example Co",
"url": "https://example.com",
"logo": "https://example.com/logo.png"
},
{
"@type": "Article",
"headline": "How AI Engines Cite Sources",
"author": { "@id": "https://example.com/#author-jane" },
"publisher": { "@id": "https://example.com/#org" },
"datePublished": "2026-03-01",
"dateModified": "2026-04-02"
}
]
}When these three schemas are connected via @id references, AI engines can verify: this article was written by [author], who works at [org], and was updated recently. That verification loop is what drives citation confidence.
What You Get in the Report
- Schema score (0-10) as part of your 7-branch audit
- List of present schemas with validation status
- List of missing schemas with priority ranking
- Copy-ready JSON-LD for each missing schema type
- Specific fixes for broken or incomplete schemas
// MISSING: FAQPage schema (high impact for AI citation)
// Copy this into your page head:
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "What is schema markup?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Schema markup is structured data..."
}
}
]
}
</script>Schema Types That Matter Most for AI
Article
essentialdatePublished + dateModified + author — baseline for any content page
Person
essentialAuthor credibility — the signal AI uses to verify expertise
Organization
essentialBrand identity — anchors the trust chain for every page
FAQPage
high impact41% citation rate with FAQPage schema vs 15% without
Product
contextualCritical for product pages, ignored elsewhere
BreadcrumbList
moderateHelps AI understand site hierarchy and page context
HowTo
tutorial-onlyHigh value for tutorials, step-by-step guides
Frequently Asked Questions
Not strictly required — AI engines can cite pages without structured data. But pages with 3+ relevant schema types are roughly 2.8x more likely to be cited. Schema lets AI verify the facts on your page programmatically: who wrote it, when, and for which organization. Without schema, AI has to infer this, and inference is less confident than verification.
Maybe. Google's rich result validator checks whether schema is syntactically valid and contains the fields needed for a specific rich result (like review stars or recipe cards). AI citation is a different bar — it cares about the entity graph (Person linked to Organization via @id), freshness (dateModified), and whether schema matches visible content. Our audit checks those AI-specific verification factors on top of standard validation.
No — we provide copy-ready JSON-LD in the audit report, and you paste it into your CMS, theme, or page template. This is intentional: automated injection risks overwriting existing markup or conflicting with plugins. Pasting a single snippet is a 1-minute job in most CMSes, and you stay in full control of what goes live.
Most modern CMSes do. WordPress supports JSON-LD via plugins like Yoast, Rank Math, or directly in theme files. Next.js supports it natively via dangerouslySetInnerHTML or the Head component. Shopify, Webflow, and Ghost all allow custom head code. If you're on a truly closed platform, the audit will flag this as a blocker and suggest alternatives.