schema markup
Structured data embedded in a page (usually as JSON-LD) that describes what the page is about in a machine-readable vocabulary defined at schema.org.
Schema markup uses the vocabulary maintained at schema.org — a joint project of Google, Microsoft, Yahoo, and Yandex since 2011 — to declare the type and properties of a page's content. A product page can declare itself a `Product`, with `name`, `price`, `Review` objects, and an `AggregateRating`. A FAQ page can declare itself a `FAQPage` with `Question` / `Answer` pairs.
The three schemas that matter most for AI citations in 2026 are `FAQPage`, `Product` (with `Review` / `AggregateRating`), and `HowTo`. These are the types LLMs are most reliably trained to read and reproduce. Other types (Article, Organization, BreadcrumbList) help but with smaller lift.
Schema is delivered as a JSON-LD block inside the page's `<head>` or inline in the body. Validity matters: a broken schema block is ignored wholesale. Always test with the Google Rich Results Test and the Schema.org validator before shipping.
In AIRRNK
AIRRNK detects schema on every scanned page, grades it for validity and completeness, and ships an auto-injection feature via the WordPress plugin and Shopify app that can generate missing schema from your existing content.
- FAQPage Schema
A specific schema.org type that declares a page contains a list of question-and-answer pairs, optimized for structured ingestion by search and AI systems.
- Generative Engine Optimization
The practice of making a website more likely to be cited by AI answer engines (ChatGPT, Claude, Perplexity, Google AI Mode) rather than simply ranked on a traditional search results page.
- AI Score
AIRRNK's 0–100 grade for how likely a site is to be cited by a language model, calculated from 47 weighted checks across four pillars.
What is Schema Markup in the context of AI SEO?
Schema Markup describes one piece of the larger Generative Engine Optimization (GEO) problem — measuring and fixing how ChatGPT, Claude, Perplexity, and Gemini talk about a business. GEO differs from classical SEO because LLM answers do not return a list of links; they return a paraphrase, and the signals that get you inside that paraphrase are different.
How does AIRank measure schema markup?
AIRank's Observer agent queries ChatGPT, Claude, Perplexity, and Gemini daily with the prompts your customers actually use and logs every mention. The Scanner agent then walks your site the way an LLM does — 47 signals across headings, schema, entity mesh, and source trust — and flags the specific gaps driving the result.
Why does schema markup matter for AI visibility?
Roughly 42% of B2B buyer research now starts inside an LLM (Forrester 2026). Pages that do not satisfy the GEO signal set get paraphrased without attribution or omitted from answers entirely — a situation Aggarwal et al. (Princeton, 2023) measured as a 30-40% citation gap against pages that do.
What is the fastest way to improve schema markup?
Start by running a free AIRank scan to surface the three highest-leverage fixes for your domain, then ship them through the Injector agent in a single click. Most teams see their first fix land within 12 minutes of install; citation lift typically shows up in weeks two and three once assistants re-crawl the edge-rewritten HTML.
Written by
The AIRank Editorial Team
Research & editorial, AIRank
The AIRank editorial team runs the 47-point scanner, the Observer pings, and the GEO research programme every week. Writing is reviewed by the core engineers who build the Injector, Blaster, and Surgeon agents.
About the team →