Structured Data Consulting
Schema Markup & Structured Data Engineering
Structured data engineered for rich results, Knowledge Graph entity recognition, and citations in AI search engines. JSON-LD that validates against Google's actual rich-result requirements — not the generic schema.org spec everyone copy-pastes.
What's Included
Comprehensive solutions tailored to your specific needs
Rich result engineering
Product, Article, BreadcrumbList, VideoObject, Event, JobPosting, LocalBusiness — schemas that still earn SERP real estate in 2025.
Entity & Knowledge Graph
Organization, Person, sameAs grounding, founder linking, and the entity work that AI engines lean on.
FAQ & HowTo schema
Even where SERP visibility is reduced, AI engines still parse them — implemented correctly per Google's current policy.
Schema validation in CI
Weekly automated validation against the production sitemap with alerts on new invalid items.
Template-level implementation
JSON-LD emitted by your theme or framework, not by three plugins fighting each other.
AI citation optimisation
On-page structure and metadata patterns that increase the rate of ChatGPT, Perplexity, and Google AI Overview citations.
Problems I Solve
Symptoms this engagement is built to fix
Product, Article, or Recipe schema marked invalid in GSC, killing rich-result eligibility entirely.
Pages with schema that validates but never earns rich results because the page does not display the data it claims.
A site with no entity strategy, invisible to Google's Knowledge Graph and to AI search engines.
FAQ schema that used to drive 15 percent of organic clicks and disappeared after Google's policy change.
Schema injected by three different plugins fighting each other on the same page.
Generic Organization schema with no sameAs links, no founder, no entity grounding.
Why most schema implementations leave value on the table
Most websites have schema. Most websites do not earn rich results, get entity recognition in Google's Knowledge Graph, or get cited by ChatGPT and Perplexity. The gap is implementation quality. Generic plugin-emitted schema validates but rarely qualifies — Google's rich result eligibility rules require the page itself to display the data the schema claims, the markup to be consistent across the site, and entities to be properly grounded with sameAs links and identifier properties. AI search engines (ChatGPT, Perplexity, Google AI Overviews) lean even more heavily on structured data when deciding what to cite. Schema is no longer an SEO bonus — it is the contract between your content and the machine readers who decide what to show humans.
Rich results that still earn clicks in 2025
Not every schema type still earns SERP real estate. Google has retired Recipe Carousels, restricted FAQ rich results to authoritative sites, and reduced HowTo to mobile-only desktop-rare positions. The schema types that still drive measurable click volume in 2025 include Product (with offers, reviews, and availability), Article (especially NewsArticle for publishers), BreadcrumbList (small visual change, real CTR lift), Event (when location and date are present), VideoObject (with proper thumbnail and clip metadata), JobPosting (for the small number of sites where it applies), and LocalBusiness for service-area businesses. Beyond the SERP, Organization, Person, and the entity-linking properties feed the Knowledge Graph and AI citation pipelines — quieter but increasingly important. My engagements prioritise the schemas that actually earn results for your industry, not a comprehensive list that mostly does nothing.
Entity grounding and the Knowledge Graph
Google does not just read your content; it builds an entity graph from it. A page about a company that includes proper Organization schema with sameAs links to its LinkedIn, Crunchbase, Wikipedia (where applicable), and primary social profiles tells Google what the entity is in a way no amount of unstructured content can. A Person schema for the founder, with worksFor pointing at the Organization and sameAs pointing at their public profiles, builds the author entity Google's E-E-A-T signals reward. For service businesses, LocalBusiness schema with proper geo coordinates, opening hours, and aggregate review properties anchors you to a physical location and to local search queries. This is the unglamorous work that compounds over years — and it is increasingly the work that decides whether AI engines cite you by name or paraphrase someone else.
Schema for AI search: ChatGPT, Perplexity, Gemini, and Google AI Overviews
AI search engines pull from a mix of structured data, citation-quality content, and entity signals. Pages that get cited consistently tend to share traits: clear Article or Q&A structure with explicit headings; FAQPage schema even where Google has reduced its SERP visibility (because LLMs still parse it); citation-grade metadata via Article schema with author, datePublished, and publisher properties; and HowTo schema where the content genuinely is a how-to. Beyond schema, the page itself should answer a specific question in the first 100 words, define terms before using them, and avoid paywalls or aggressive client-side rendering. My engagements cover both the structured data and the on-page patterns that AI engines reward, because schema without citable content does not move the needle.
Implementation: how the schema is shipped and maintained
JSON-LD in the head of every page, emitted by the template layer or framework rather than a plugin. WordPress: theme functions or a small custom plugin (not Yoast + Rank Math + a third tool fighting each other). Shopify: theme.liquid emission via liquid template, not an app. Next.js: a typed Schema component per page with a shared types definition. Validation: Google's Rich Result Test for every changed template, plus a CI step that runs schema validation against the production sitemap weekly. Monitoring: GSC's Enhancements reports per schema type, with alerts on new invalid items. This is the implementation that does not silently drift into invalidity six months after launch.
What You Receive
Deliverables & expected outcomes
- Site-wide schema audit with priorities by rich-result value
- Template-level JSON-LD implementation
- Entity grounding for Organization, Person, and (where applicable) LocalBusiness
- GSC Enhancements report monitoring setup
- CI validation against Google's Rich Result Test
- AI search citation optimisation document
Illustrative Engagement
A representative scenario
The problem
A B2B publication had Article schema on every post but no rich results in GSC, and was almost never cited by Perplexity or Google AI Overviews despite ranking strongly for the underlying queries.
The approach
Audit found Article schema was missing author, publisher, and dateModified properties, and that the author Person schema linked to no external profiles. We rebuilt Article emission from the template, added properly grounded Person schemas for the four regular contributors with full sameAs blocks, and added FAQPage schema to the buyer-guide pages.
The outcome
Rich-result eligibility moved from near zero to 84 percent of articles, and Perplexity citation rate for the target query set rose from 6 percent to 41 percent over the following two months.
Composite scenario based on typical engagements. Replace with a real, attributed case study once published.
My Process
A proven methodology for delivering exceptional results
Schema audit
Site-wide audit of current schema validity, completeness, and rich-result eligibility — plus an entity gap analysis.
Strategy
Prioritised schema roadmap by rich-result value and AI citation likelihood, with a written entity grounding plan.
Implementation
Template-level JSON-LD emission, plugin/app consolidation, and entity property completion.
Monitor & iterate
GSC Enhancements monitoring, weekly CI validation, and quarterly review as Google's rich-result policies evolve.
Why Choose Me?
With 7+ years of experience and a track record of delivering results, I bring expertise, reliability, and a results-driven approach to every project.
- Schema that actually earns rich results, not just validates
- Entity grounding that feeds the Knowledge Graph and AI engines
- Template-level emission — no plugin chaos
- CI validation so schema does not silently drift to invalid
- AI search citation patterns built in
- Senior consultant delivering directly — no junior handoff
Starting at
€297
Schema audit, template-level JSON-LD implementation, entity grounding, AI citation optimisation, and CI validation.
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