How do I add Organization schema for AI search?
Add a JSON-LD script tag with Organization schema to your root layout (head). Include name, legalName, url, logo, foundingDate, founders, sameAs links to LinkedIn/GitHub/Crunchbase, and disambiguatingDescription if a similarly-named company exists. AI engines re-crawl and update within 7-14 days.
Organization schema is the foundation of entity recognition for AI engines. Every page on your site should include it (or inherit it from the root layout). Three steps to ship correctly.
Step 1: Draft the schema
The minimum-viable Organization schema includes 8 fields. Most B2B SaaS sites ship 3 and stop. Ship all 8 from day one.
- name — exact brand name as you want AI engines to refer to it.
- legalName — registered legal entity, if different from name.
- url — canonical homepage URL with https:// and trailing slash.
- logo — absolute URL to a square SVG or PNG logo (256x256 or larger).
- foundingDate — ISO 8601 date the company was founded.
- founder — Person schema for each founder with name, jobTitle, sameAs.
- sameAs — array of LinkedIn, GitHub, Crunchbase, X/Twitter URLs.
- disambiguatingDescription — only if a similarly-named company exists; clarifies which one you are.
Step 2: Inject in the root layout
In Next.js App Router, add a JSON-LD script tag to the root layout (or to a component imported by it). Render via a safe primitive that escapes script-terminator sequences. NEVER use dangerouslySetInnerHTML for JSON-LD — risk of XSS via unescaped content. Use a children-as-text pattern inside a <script type="application/ld+json"> tag.
Pixelmojo open-sources its <JsonLd> primitive in /components/seo/JsonLd.tsx. Safe by construction (escapes </script>, U+2028, U+2029, &, <, >).
Step 3: Validate before deploy
Two free validators to run: Google Rich Results Test (https://search.google.com/test/rich-results) confirms Google parses your schema. Schema.org Validator (https://validator.schema.org/) confirms the schema is well-formed per the spec. Both should pass before deploying.
| Validator | What it checks | When to use |
|---|---|---|
| Google Rich Results Test | Google-specific schema interpretation | Before deploying to verify Google sees what you intend |
| Schema.org Validator | Spec compliance (broader than Google) | For non-Google AI engines (Claude, Perplexity, Gemini) |
| Radar Schema Audit | Coverage + completeness across all pages | Audits whole site, not just one URL |
How to audit Organization schema coverage on your site
Radar Schema Audit crawls your site and reports which pages have Organization schema, which are missing critical fields (legalName, founders, sameAs), and what to add. Free first audit covers the full site.
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Related questions
Schema.org vs llms.txt: which AI search signal matters most?
Both matter and they do different jobs. Schema.org JSON-LD identifies your entities (Organization, Product, Person) per-page for machine parsing. llms.txt is a site-level declaration of what your site is about, what content to prioritize, and how AI engines should use it. Ship both.
WhyWhy is not ChatGPT citing my B2B SaaS?
ChatGPT cites sites it treats as authoritative for a query. Most B2B SaaS sites lack three specific signals: structured data declaring entity identity, an llms.txt file, and citations from domains ChatGPT already trusts.
WhyWhy is my company invisible in AI search results?
AI engines surface companies they trust as authoritative for a query. Invisibility usually means one of three things: AI crawlers cannot access your site, your structured data does not identify your entity, or no high-authority source on the web mentions you.
DefinitionWhat is Generative Engine Optimization (GEO)?
Generative Engine Optimization (GEO) is the practice of structuring web content so AI search engines like ChatGPT, Claude, Perplexity, and Gemini cite it in their responses. Unlike SEO which optimizes for keyword rankings, GEO optimizes for entity recognition, structured data, and citation probability.
