Services
Schema Markup Audit Services & Consultant
Structured data audited against what Google and AI systems actually parse — not just whether it validates.
Paul Lovell — Technical SEO Consultant
Who this is for
Sites with schema markup already in place that isn't producing rich results, isn't being cited in AI Overviews, or was bolted on by a plugin/agency and never checked against the entity graph.
Process
- 1Full structured data crawl — every JSON-LD block extracted and mapped against page type
- 2Validation against Schema.org vocabulary and Google's actual supported rich-result types, not just syntax checking
- 3Entity graph review — @id cross-referencing between Organization, Person, and Product/Service entities
- 4AI citation readiness check — whether the markup gives LLMs and AI Overviews enough structured signal to cite the page directly
- 5Prioritised fix list ranked by rich-result and citation impact, not error count
How this actually works
Not a checklist — the specific mechanisms most audits skip past.
01
Validate against Google's supported rich-result types, not just Schema.org syntax
Schema.org's vocabulary is broader than what Google actually renders as a rich result. Markup can be 100% valid and still do nothing in the SERP because the page type isn't eligible, or because a required property for that specific rich-result type is missing — a generic validator won't tell you which properties are cosmetic and which are load-bearing.
02
Cross-reference entities with @id instead of duplicating them
The same Organization or Person entity written out separately in every page's schema is a missed signal, not just duplication. Referencing a single @id across Organization, Person, and Service/Product entities lets Google (and AI crawlers) build one confident entity graph instead of several unlinked fragments — this is usually the single biggest gap in bolted-on plugin schema.
03
Check what's actually in the rendered DOM, not just the template source
JSON-LD injected client-side via a tag manager or a schema plugin can be present in the rendered page but missing or delayed in what Googlebot's first crawl wave sees. Diffing the raw HTML response against the rendered DOM catches this before it silently costs you a rich-result slot.
04
Test AI-citation readiness separately from rich-result eligibility
Getting cited in an AI Overview or LLM answer isn't the same test as rich-result eligibility — it depends on whether the structured data gives a clear, self-contained answer to the query (FAQ/HowTo schema, explicit Answer properties, unambiguous entity naming) rather than just validating. A page can pass every rich-result check and still give an AI system nothing quotable.
Sample finding — entity graph fragmentation
Organization entities found across site: 4 separate blocks Cross-referenced via @id: 0 Page A (/about): "Paul Lovell" as Person, no @id Page B (/services): "Paul Lovell" as Person, different @id Page C (/blog/*): Author block, name string only, no entity Impact: Google cannot confirm these refer to the same entity. Recommendation: single Person + Organization @graph, referenced by @id from every page — not redeclared per template.
Deliverables
Frequently asked questions
What's the difference between a schema markup audit and just running a validator?
A validator checks syntax — is the JSON-LD well-formed and does it match Schema.org's vocabulary. An audit checks whether that markup is actually doing anything: is the page type eligible for a rich result, are the load-bearing properties present, does the entity graph cross-reference correctly, and is there enough explicit signal for AI systems to cite the page. Markup can pass every validator check and still be functionally useless.
Why isn't my schema markup producing rich results?
The most common causes: the page type isn't eligible for the rich result you're expecting, a required (not just recommended) property is missing, the markup is injected client-side and missed by Google's first crawl wave, or there's a mismatch between what the schema claims and what's visibly on the page — Google can ignore or penalise markup that doesn't match visible content.
Does schema markup help with AI Overviews and AI citations?
It can, but it needs different signals than rich-result eligibility — clear FAQ/HowTo structure, explicit entity naming that doesn't rely on context, and a self-contained answer an LLM can lift without ambiguity. Generic Organization/Product schema alone doesn't move this; it's usually the content structure plus schema together that gets a page cited.
How long does a schema markup audit take?
A single-site audit typically takes 3-5 days depending on template count and how many page types carry structured data. Implementation timelines depend on your dev resource — the audit deliverable is corrected JSON-LD ready to drop into each template, not a request to rebuild your CMS.