Schema Markup Audit for AI Search: A 30-Minute Checklist

A schema markup audit for AI search checks whether your JSON-LD is valid, appropriate for the page, consistent with visible copy, and present on the deployed URL. It does not hunt for a secret AI-only schema. Google says AI Overviews and AI Mode require no special Schema.org markup, and structured data does not guarantee that a page will be shown. The useful outcome is accurate machine-readable context, not a citation promise.

What the audit should prove

A useful audit answers four questions: Is the JSON-LD valid? Is the type suitable for this page? Do its facts agree with what a visitor can see? Does the production URL actually deliver it? A green validator answers only part of that list. Google's structured data guidelines also require markup to represent the page truthfully and warn against hidden, irrelevant, misleading, or stale claims.

1. Choose one URL for each page job

Start with templates, not your entire sitemap. Pick the homepage and one live example of every high-value page job you publish: service, feature, product, article, FAQ, and another inner page. If those templates generate the markup, one representative URL can reveal a site-wide error without turning a 30-minute check into a crawl project.

That table is a practical starting roster, not a universal requirement that every site needs every type. Answer Patch's Structured Data pillar documents the page-type expectations its scoring engine actually checks, including when a rule is skipped. Schema.org itself defines types such as Organization, LocalBusiness, FAQPage, and BreadcrumbList; Google Search documentation remains the authority for Google-specific feature eligibility.

2. Test vocabulary and Google eligibility separately

Run two validators because they answer different questions. The Schema Markup Validator checks Schema.org-based markup without Google-specific feature warnings. Google's Rich Results Test checks which supported Google rich results the page may qualify for. Google recommends both tools for those separate jobs in its structured data testing guide.

1. Paste the live URL into Schema Markup Validator. Record syntax errors, unrecognized properties, and every detected entity type.
2. Paste the same live URL into Rich Results Test. Record blocking errors separately from optional-field warnings.
3. If a tool finds no markup, inspect the deployed page source and rendered DOM. Confirm whether a plugin or client script failed to emit the JSON-LD you expected.
4. Save the URL, date, detected types, and error text. A short evidence log makes the retest comparable.

3. Compare every marked-up fact with visible copy

The highest-value manual step is a field-by-field truth check. Open the page beside its extracted JSON-LD. For each name, phone number, address, price, review count, author, date, question, answer, and profile URL, point to the visible or otherwise verifiable source. If you cannot support a field, remove it instead of asking a machine to trust it.

Google's structured data introduction says the markup belongs on the page it describes, should not add information hidden from users, and is better with fewer complete and accurate properties than many incomplete or inaccurate ones. It also recommends JSON-LD for most sites because it is usually easier to implement and maintain.

4. Fix correctness before adding coverage

Prioritize errors that make a machine read the wrong thing. More schema is not automatically better, and a missing optional type is not the same as a false claim. Fix the audit in this order so the most consequential contradictions disappear first.

1. Repair JSON syntax or a broken @context, @type, or required property that prevents parsing.
2. Remove fabricated, hidden, stale, or contradictory facts, including reviews and profiles you cannot verify.
3. Correct the page type and any required fields for the search feature you actually want to support.
4. Replace relative, redirected, or dead URLs in url, logo, image, sameAs, and breadcrumb items.
5. Only then add a missing useful type for a real page job. Skip optional markup that has no visible facts to describe.

Do not let a generic schema generator choose facts on your behalf. Drafting code is the easy part; deciding which entity, relationship, and claim the page can honestly support is the editorial work. The same rule applies to AI-assisted JSON-LD: label the draft, review every field, and validate the final deployed output.

5. Retest the deployed page, not a code snippet

A pasted snippet can pass while production still fails. A CMS template may escape JSON, a plugin may emit a duplicate entity, or a deployment may omit the script on one route. Run both validators against the public URL after release, then view the page source and confirm the expected entity and current facts are present.

• Retest one URL from every changed template.
• Check that duplicate plugins did not publish conflicting Organization, Product, or Article nodes.
• Confirm canonical, image, logo, and breadcrumb URLs return the destinations you intended.
• Use Search Console's URL Inspection and rich-result reports when Google-specific discovery or deployment status matters.
• Repeat the audit after a redesign, CMS migration, business-name change, phone change, or schema-plugin update.

What this schema audit cannot prove

A clean schema audit cannot prove that ChatGPT, Perplexity, Google AI Overviews, or another system will rank, cite, recommend, or summarize the page. It also does not prove that crawlers can reach the page, that the visible answer is useful, or that the business has enough public evidence to support a recommendation. Those are separate access, content, and trust questions.

Common schema-audit questions

These answers keep the audit focused on what current public documentation supports, rather than treating structured data as an AI citation shortcut.

Does schema markup improve AI search visibility?

Structured data can help Google understand a page and enable eligible search features, but Google says there is no special Schema.org markup required for AI Overviews or AI Mode. Valid markup does not guarantee indexing, serving, or citation. Treat schema as accurate machine-readable infrastructure, not a direct visibility promise.

Do I need FAQPage on every page?

No. Use FAQPage only where visitors can read the questions and answers it describes. Answer Patch does not treat missing optional FAQPage on a homepage as a deficiency, but it does flag FAQ markup that is malformed or contradicts visible copy. The FAQPage glossary entry explains the visible-content boundary.

Which schema testing tool should I use?

Use both official tools. Schema Markup Validator checks general Schema.org vocabulary, while Google's Rich Results Test checks eligibility for Google-supported rich-result features. Then manually compare the extracted facts with the visible page, because a validator cannot decide whether a business claim is current and true.

Your next action

Pick your homepage and one high-value inner page. Run both validators, compare every material field with visible copy, fix contradictions before omissions, and retest the live URLs. If the markup is accurate and appropriate, stop adding types for volume and move to the next verified access, content, or trust gap.