Schema Markup Audit Checklist for AI Extraction

Optimizing schema for AI extraction mostly means auditing what you already have: is it present on the right pages, accurate, consistent with your visible content and connected into one clear picture of your business? Most sites have schema that is incomplete or out of date. Use this checklist to find and fix the gaps.

Part 1: Coverage

• Your homepage has Organization (or LocalBusiness) markup with name, logo, URL and contact details.
• Each physical location has its own LocalBusiness block with address, opening hours and phone number.
• Product pages have Product markup with name, description, price and availability.
• Blog posts and guides have Article markup with author and dates.
• Key service pages have Service markup or at least a clear description tied to your organization.

Part 2: Accuracy

• Prices in the markup match the prices on the page today.
• Opening hours include holiday or seasonal changes where possible.
• Phone numbers and addresses match your Google Business Profile exactly.
• Discontinued products are removed or marked out of stock.

Stale markup is a common reason AI tools repeat old facts. If you have already seen this happen, our guide on fixing wrong pricing in ChatGPT covers the wider clean-up.

Part 3: Entity connections

• Your Organization markup uses sameAs to link your official profiles: LinkedIn, X, Facebook, Instagram, YouTube, Crunchbase, Wikidata if you have an entry.
• Every schema block on the site refers to the same organization, ideally with a stable @id such as your homepage URL plus #organization.
• Authors are marked up as Person with links to their profiles.
• Products and services reference your organization as brand, manufacturer or provider.

These links help machines understand that all these pages and profiles are about one business. Our page on entity SEO for AI explains why this matters.

Part 4: Consistency with the page

• Nothing in the markup is hidden from users.
• Review ratings in markup come from real reviews shown on the page, following Google’s review guidelines.
• The description in markup reads naturally and is not stuffed with keywords.

Part 5: Technical health

• Pages pass the Schema.org validator with no errors.
• Google’s Rich Results Test reads the markup on mobile.
• No duplicate or conflicting blocks from two plugins or a theme plus a plugin.
• Markup is in the HTML Google receives, not only injected late by scripts.

Part 6: Upkeep

• Someone owns schema updates when prices, hours or staff change.
• You re-run the audit every quarter or after a site redesign.

How to prioritise fixes

Start with errors that spread wrong facts: prices, hours, addresses. Then fix entity links, then fill coverage gaps. Schema supports AI extraction, but it does not replace clear, useful content, so pair each fix with a check of the visible page too.

A worked example

Take a physiotherapy practice with two clinics. The audit finds Organization markup on the homepage but no LocalBusiness blocks, so both clinics share one address in the markup. Saturday hours were added to the website last spring but never to the schema. The Instagram profile is missing from sameAs. And a theme and a plugin both output Organization markup with slightly different names. The fix list, in order: correct the hours, add one LocalBusiness block per clinic, remove the duplicate block, then add the missing profile link. Each item takes minutes once it is found. The audit is the hard part.

How Keboost helps

Keboost tracks what AI assistants say about your brand across real buyer prompts, which shows you where wrong or missing facts are hurting you. It then helps you publish clear, consistent content to fix them. Run a free AI visibility check to see which facts AI tools are getting wrong.