Why structured data matters for AI
Unstructured content requires AI to infer relationships. Structured data makes relationships explicit.
When AI encounters "Acme was founded in 2019", it must determine that Acme is a company and 2019 is a founding date.
With Schema.org markup, you state explicitly: Organization, foundingDate: 2019. No inference needed.
Structured data doesn't guarantee AI will use your information, but it dramatically reduces misinterpretation.
Essential Schema.org types
Organization - Your company entity. Include: name, foundingDate, founders, numberOfEmployees, address, sameAs (social profiles)
Product or SoftwareApplication - Your products. Include: name, description, offers (pricing), aggregateRating, featureList
FAQPage - Real question and answer content. Useful as optional AI-clarity markup, but no longer a Google FAQ rich-result lever.
HowTo - Step-by-step guides. AI can extract and cite these directly.
Review - Customer feedback with ratings. Contributes to sentiment perception.
Person - Founders, executives, key team members. Helps with entity disambiguation.
Implementation basics
Use JSON-LD format in your <head> tag:
<script type="application/ld+json">
{"@context": "https://schema.org", "@type": "Organization", "name": "Acme Software", "foundingDate": "2019", "numberOfEmployees": "150"}
</script>
Every page should have relevant schema. Product pages need Product schema. About pages need Organization schema. FAQ pages can use FAQPage only when it mirrors visible Q&A.
Test syntax with Schema.org validator. Use Google's Rich Results Test only for rich-result types Google still supports; FAQ support is being removed.
The llms.txt standard
llms.txt is an emerging standard for communicating with AI systems directly. Think robots.txt but for language models.
Place at /llms.txt on your domain. Include:
# Brand Information
Name: Acme Software
Description: B2B inventory management platform
Founded: 2019
Headquarters: Berlin, Germany
# Key Facts
- Serves 10,000+ businesses
- Available in 12 languages
- SOC 2 Type II certified (only if true and current)
While not universally adopted yet, it's a low-effort addition that helps forward-thinking AI systems.
Advanced: Entity linking
Connect your entities to authoritative databases:
sameAs: Link to your Wikipedia page, Crunchbase profile, LinkedIn company page
identifier: Include stock tickers, DUNS numbers, or other business identifiers
@id: Use consistent URIs for entities across your site
Entity linking helps AI understand you're the same "Acme" mentioned in other sources, not a different company.
Common mistakes to avoid
1. Incomplete schema - Partial data is worse than no data. If you add Product schema, include all relevant fields.
2. Inconsistent across pages - Your Organization schema should be identical everywhere it appears.
3. Outdated information - Schema with old pricing or discontinued features causes accuracy issues.
4. Over-optimization - Don't stuff schema with keywords. AI systems can detect this.
5. Missing on key pages - Homepage, pricing, and about pages absolutely need structured data.
What structured data can and cannot do
Schema helps machines identify entities, content types, authorship, products, FAQs, videos, and breadcrumbs. It does not make a weak page authoritative.
Use structured data to reinforce truth already visible on the page. Never hide claims in JSON-LD that users cannot see. That creates a public truth split.
Minimum useful stack
For most GEO pages, start with Article or WebPage, BreadcrumbList, Organization, Product or SoftwareApplication when relevant, and VideoObject/ImageObject for meaningful media. Add FAQPage only when real visible FAQs exist, and treat it as optional.
Keep llms.txt and public facts aligned with the same canonical truth. If pricing changes, update visible copy, schema, docs, and AI-readable files together.