Schema Markup for AI Search

Schema markup helps AI search indirectly: it makes facts about your organisation, products and content unambiguous for search engines, whose indexes AI answers often draw on. It isn't a shortcut into AI answers. Google says there's no special schema for its AI features (Google). But consistent, accurate markup reduces the room an engine has to guess.

On this page

• What does Google say about schema and AI features?
• Which schema types matter most?
• From MentionX data: why entity clarity matters
• Why is Organization markup the place to start?
• Which Organization properties are worth filling in?
• How do you mark up a SaaS product?
• How should articles show authorship?
• What's the "match rule" and why does it matter?
• How do you implement and validate?
• Worked example: Ledgerline's schema audit
• How do you prioritise schema work?
• How does schema fit with the rest of your entity signals?
• How do you measure whether schema work helped?
• What mistakes come up most often?
• What does schema not do?
• Common questions
• Sources

Checked against Google Search Central and schema.org on 30 October 2026. "Our view" marks MentionX opinion. Ledgerline is fictional.

What does Google say about schema and AI features?

In its guide for site owners, Google lists "making sure your structured data matches the visible text on the page" as an SEO practice that remains worthwhile, and states: "There's also no special schema.org structured data that you need to add" to appear in AI Overviews or AI Mode (Google Search Central).

Google's general structured data guide explains that markup helps Google understand page content and can enable rich results (Google: intro to structured data). That understanding is the part that carries over to AI features.

Which schema types matter most?

From MentionX data: why entity clarity matters

Method. Read-only aggregate queries over completed MentionX runs from 22 August to 29 October 2026 (UTC): 2,154 runs across 18 anonymised projects and six engines, with 9,750 citations from 2,152 domains.

Among non-Gemini citations we could classify (n = 5,917), homepages made up 10.0%, review sites 2.7%, docs or help pages 2.6%, pricing pages 1.9% and Wikipedia 1.6%.

Two points follow. First, many mentions come with no supporting citation, so the engine relies on what it learned or retrieved earlier about the brand. Second, when engines do cite, homepages are the second-largest type we can classify, after blog and guide pages. Your homepage, and the Organization markup on it, is one of the pages most likely to be read.

Limits. We can't see whether an engine parsed a page's JSON-LD, so these figures don't prove that schema causes mentions. Gemini's citations are mostly redirect links and are excluded from the URL types.

Our view: schema is a consistency tool. It's worth the time because it's cheap and lines up with everything else that describes you, not because it directly causes citations.

Why is Organization markup the place to start?

Answer engines describe you badly when the web describes you inconsistently. Organization markup is where you declare the canonical facts: legal and brand name, URL, logo, and sameAs links to your official profiles (schema.org/Organization).

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "MentionX",
  "url": "https://mentionx.ai",
  "logo": "https://mentionx.ai/logo.png",
  "description": "AI visibility platform that tracks how AI assistants mention, cite and recommend brands.",
  "sameAs": [
    "https://www.linkedin.com/company/mentionx-ai/",
    "https://www.youtube.com/@mentionxai",
    "https://x.com/mentionxai"
  ],
  "founder": {
    "@type": "Person",
    "name": "Dipuck Jones",
    "sameAs": ["https://www.linkedin.com/in/dipuckjones/"]
  }
}

Rules of thumb:

• Use the same description wording you use on LinkedIn and in directory listings.
• Only list profiles you control in sameAs.
• Put it on the homepage (or about page), once.

Which Organization properties are worth filling in?

Schema.org lists dozens of Organization properties (schema.org). Most sites need a handful, and Google documents which ones it uses (Google).

How to choose `sameAs` links. Include profiles that describe the same organisation and that you control or that clearly represent you: LinkedIn, YouTube, X, GitHub, Crunchbase, your Wikidata item if one exists. Leave out review pages you don't control, partner pages and old social accounts. Each link should strengthen the same picture.

How do you mark up a SaaS product?

For software, SoftwareApplication with an Offer states category, platform and price in a form engines can read.

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "Ledgerline",
  "applicationCategory": "BusinessApplication",
  "operatingSystem": "Web",
  "offers": { "@type": "Offer", "price": "39", "priceCurrency": "EUR" }
}

Fill in what you show on the page and nothing more. If pricing is "contact sales", leave out the offer rather than inventing a number. If you add aggregateRating, the reviews must be visible on the same page.

How should articles show authorship?

For guides you want cited, mark up the author and dates, and make them visible:

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Schema Markup for AI Search",
  "author": { "@type": "Person", "name": "Dipuck Jones", "url": "https://mentionx.ai/authors/dipuck-jones" },
  "datePublished": "2026-10-30",
  "dateModified": "2026-10-30",
  "publisher": { "@type": "Organization", "name": "MentionX" }
}

This matches Google's emphasis on clear authorship as part of helpful, reliable content (Google: helpful content).

What's the "match rule" and why does it matter?

Every claim in your markup should be visible on the page. Common violations:

• Product price in JSON-LD that differs from the price shown.
• aggregateRating with no visible reviews.
• FAQ markup for questions that aren't on the page.
• Organization description that says something your homepage doesn't.

Mismatches can make you ineligible for rich results and, worse, give engines conflicting facts to repeat.

How do you implement and validate?

1. Inventory existing markup with a crawler (Screaming Frog, Sitebulb) or by viewing source on key templates.
2. Fix the templates, not individual pages: homepage, product, article, author.
3. Validate with Google's Rich Results Test and the Schema Markup Validator.
4. Monitor Search Console's enhancement reports for errors.
5. Re-check after every redesign; markup often breaks silently.

Worked example: Ledgerline's schema audit

Ledgerline is a fictional accounts-payable tool. Its SEO lead audits four templates with a crawler and the Schema Markup Validator.

The pricing mismatch is the most damaging: it gives engines an old price to repeat. The team moves all four templates to a single shared data file, so the markup and the visible page are generated from the same values. The description field now uses the same sentence as the LinkedIn About section and the llms.txt summary.

How do you prioritise schema work?

How does schema fit with the rest of your entity signals?

Markup is one of several places that describe who you are. Line them up so they say the same thing:

• Homepage H1 and subhead
• Organization description and sameAs
• LinkedIn company "About" text
• Review-site and directory profiles
• llms.txt summary, if you publish one

When all five agree on name, category and one-line description, answer engines have far less room to guess.

How do you measure whether schema work helped?

Be honest about what you can attribute. Schema changes rarely move AI visibility on their own, and many other things change in the same weeks. Track three levels:

1. Technical health. Validation errors and warnings in the Rich Results Test, the Schema Markup Validator and Search Console's reports. These should drop to zero on key templates. This is the part you can fully control.
2. Accuracy of what engines say. Run a fixed set of brand prompts, such as "What is Ledgerline?", "How much does Ledgerline cost?" and "Who makes Ledgerline?", in several engines before and after. Score each answer as correct, outdated or wrong for category, pricing and company facts. Repeat each prompt several times: in MentionX data, 11 of 28 prompt-engine pairs repeated over six weeks gave mixed results on whether the brand was mentioned at all.
3. Visibility. Mention and citation rates on unbranded prompts. Expect these to depend mostly on content and third-party coverage, not markup.

If the accuracy score improves while visibility stays flat, the schema work did its job. Its job is to make sure that when you're mentioned, you're described correctly.

What mistakes come up most often?

• Several Organization blocks on one site, each with slightly different names or logos, often from a theme plus a plugin.
• Markup for the wrong entity, such as a parent company's details on a product site.
• Stale prices and plan names in Offer markup after a pricing change.
• Copying JSON-LD from a template and leaving the placeholder values in.
• Reviews markup on pages with no visible reviews, which goes against Google's guidelines.

What does schema not do?

• It doesn't override content quality. A thin page with perfect markup still won't be cited.
• It doesn't get you into AI Overviews by itself (Google).
• It isn't read by every AI tool. Treat clean, visible facts on the page as the primary signal and markup as the confirmation.

Common questions

JSON-LD, Microdata or RDFa? Google supports all three and recommends JSON-LD because it's easiest to maintain (Google).

Should I still use FAQPage markup? It's valid schema.org, but Google stopped showing FAQ rich results. Use it only where the questions are visible on the page, and don't expect a visual change in Google.

Does markup on other sites about me count? It helps if review sites and directories mark up your product consistently, but you can't control it. Keep your own profiles accurate.

Does schema help in ChatGPT or Perplexity? Neither company documents using structured data to pick sources. Their search features rely on crawled pages and search indexes, so markup that helps search engines may help indirectly. Visible text on the page is what any engine can read.

Where should JSON-LD go on the page? Anywhere in the HTML, usually the head. Google can process markup added by JavaScript, but other crawlers may not run scripts (Google). Putting it in the HTML your server sends is the safer choice, and it's easier to check with a plain request.

How often should I re-check? After every redesign, CMS migration or pricing change, and once a quarter otherwise. Markup usually breaks quietly, through a theme update or a removed plugin, and nobody notices until a validator or an AI answer shows the old facts.

Can plugins handle all this? They handle the basics well. Check their output: plugins often add generic Organization details or skip sameAs unless you fill in the fields.

Sources

• Google Search Central, AI features and your website
• Google Search Central, Introduction to structured data; Organization structured data
• Google Search Central blog, Changes to HowTo and FAQ rich results
• schema.org/Organization, the full list of Organization properties, including sameAs, founder and description
• Google Search Central, Creating helpful, reliable, people-first content, on authorship and trust
• Google Search Central, Documentation updates, accessed 30 Oct 2026
• schema.org/sameAs
• Schema Markup Validator and Google Rich Results Test