Tell the machine what your page actually is.
Schema is the machine-readable layer beneath your content — the explicit declaration of what each page is, what it offers, who it serves, and how it connects to the rest of the world. Done well, it is what lets a model read your business correctly the first time.
Six markup layers.
One unambiguous source of truth.
Schema is not a single deliverable that ships once and stays solved. It is a coordinated set of vocabularies, each describing a different facet of your business, and each requiring its own implementation, validation, and ongoing maintenance.
Every engagement begins with a structured-data audit, every type is validated against Google’s tools and the Schema Markup Validator, and every page that ships is verified end-to-end before we move on.
Organization & Brand Schema
Identity layerYour Organization markup is the schema equivalent of a passport. It declares your legal name, your operating name, your founding date, your founders, your logo, your social profiles, your contact points, your service areas, and your sameAs links into Wikipedia, Wikidata, LinkedIn, and the directories that matter. Done correctly, it is the single piece of structured data most likely to land your business in a model’s knowledge graph as a recognised entity rather than a string of ambiguous text. Done badly, it muddles the brand across every downstream signal.
LocalBusiness & Service Markup
Geographic clarityFor any business with a physical presence or a defined service area, LocalBusiness and its specialised subtypes — LegalService, MedicalBusiness, FinancialService, Restaurant, and the rest — give models the exact category they need to answer location-aware questions correctly. We pair the type declaration with explicit address, phone, hours, geo coordinates, accepted payments, and area-served polygons where appropriate. The result is a business that AI models can confidently surface when a user asks for “the best immigration lawyer in Vancouver” rather than guessing whether you serve that city at all.
Person, Author & Expertise Markup
Trust signalsFor service businesses where credentials matter — legal, medical, financial, consulting — Person schema declares the practitioners behind the work. Names, titles, qualifications, professional affiliations, alma maters, and knowsAbout topic markup tell a model exactly who is qualified to advise on what. We tie author markup to article and FAQ schema so that models can attribute every answer on your site to a named, verifiable expert rather than an anonymous content farm. In a world where AI answers must be defended on credibility, named authorship is no longer optional.
FAQ, HowTo & Article Schema
Answer-readyWhen a model is asked a question, the cleanest possible source is one that has already declared, in structured form, that a given answer corresponds to that exact question. FAQPage, HowTo, and well-formed Article markup do precisely that — they let a model lift quotable, attributable text without guessing where the question stops and the answer begins. We map your existing content to the right schema types, restructure pages where the underlying copy needs help, and validate every implementation against the rich-result tests so the markup actually earns the visibility on offer.
Product, Offer & Review Schema
Commerce layerFor e-commerce and product-led businesses, Product, Offer, AggregateRating, and Review markup is what lets a model recommend you accurately when a buyer asks for product comparisons, price ranges, or category leaders. We deploy schema across catalogue and detail pages, declare price, currency, availability, GTIN, brand, and review aggregates, and keep the data fresh through automated feeds rather than stale hard-coded values. The work pays off in both classic rich snippets and the new generation of AI-assisted shopping conversations now appearing across ChatGPT, Perplexity, and Google AI Overviews.
Entity & Knowledge-Graph Markup
Semantic webBeyond the obvious page-level types, schema becomes most powerful when it declares the relationships between entities — your business, its people, its services, its locations, its parent organisations, its industry associations. We use sameAs, parentOrganization, memberOf, knowsAbout, and about to build a small, tidy knowledge graph specific to your business. We also coordinate Wikidata entries, structured-data presence in industry registries, and the consistent @id URIs that let models stitch your entity together across sources. This is the work that turns scattered mentions into a single recognised brand.
Audit. Declare.
Validate. Maintain.
Schema is one of those disciplines where the wrong answer ranks worse than no answer. Bad markup mismatches your real content, contradicts other signals, and trains a model to ignore your domain. We treat structured data as production data — written carefully, validated rigorously, kept current.
Audit before any markup ships
The first deliverable is a written structured-data audit covering every existing schema implementation, every Google rich-result eligibility, every validation error, and every place schema should exist but does not. We work from this document and you keep it as a reference. Sites that have layered three different SEO plugins over five years are the most common scenario, and untangling that history is half the work.
JSON-LD over microdata, every time
Google, Bing, and the major AI crawlers all prefer JSON-LD blocks in the page <head> over inline microdata or RDFa. JSON-LD separates structured data from presentational HTML, survives template changes, and is straightforward to maintain at scale. We default to JSON-LD for every new implementation and migrate legacy microdata as we go, unless a specific platform constraint argues otherwise.
Validate against multiple tests
Every schema block is checked through Google’s Rich Results Test, the Schema Markup Validator, and our internal entity-consistency checks. We do not declare an implementation finished because a single tool turned green. Different tests surface different errors — Google’s test is conservative, the Schema.org validator is strict on vocabulary, and entity checks catch the cross-page contradictions that neither tool sees.
Keep it accurate, forever
Stale or contradictory structured data is worse than no structured data — it actively trains models to distrust your domain. We pair every implementation with a maintenance plan: automated feeds for product data, scheduled audits for content schema, and a documented protocol for any team member who edits the underlying content. Schema is not a one-time build; it is a small piece of infrastructure that needs ongoing care.
Businesses serious
about being read
correctly.
Schema is the right starting point for any business preparing for AI visibility — but it is also the necessary foundation for traditional rich results, e-commerce shopping graphs, and anywhere else a search system needs an unambiguous read on what your page actually is.
Schema work pays off most for businesses where every citation has to be defensible.
-
i
Service businesses with credentialed practitionersLaw firms, medical practices, accountancies, consulting firms.
PersonandServiceschema turn named expertise into structured data a model can attribute, defend, and surface in answers. -
ii
Local businesses with a defined service areaAnyone where geography is part of the value — clinics, agencies, tradespeople, restaurants.
LocalBusinessmarkup is the difference between being recommended for “in Vancouver” and being misplaced in Toronto. - iii E-commerce and product-led brandsWhere buyers are increasingly asking AI assistants for product comparisons, recommendations, and price checks. Product, Offer, and Review schema is what lets your catalogue participate in those conversations rather than sit them out.
- iv Publishers, educators, and content-led businessesAnywhere articles, courses, FAQs, or how-to content sits at the heart of the audience relationship. Article, FAQ, and HowTo schema make the underlying content directly extractable by models — and citation-ready.
- v Multi-location and multi-entity organisationsFranchises, chains, holding companies, professional networks. Entity markup is what stitches a sprawling brand into a single, model-recognised organisation rather than a confusing mesh of duplicates.
Schema is also the starting point for almost every other AI search investment. Citation-building, per-LLM optimisation, and visibility audits all rely on a clean entity-level read of your business as their foundation. If the structured-data layer is wrong, every other layer inherits the error.
Curious how Google sees your site?
Send us your URL. We’ll send back a Premium SEO Report, prepared by hand, within 48 hours — domain authority, keyword rankings, backlinks, competitor gap, and the schema and entity gaps worth closing first. The same report doubles as your starting point for AI search visibility, because the foundations are shared.
No sales call required.
Explicit data is a kindness to the crawler. Tell the machine plainly what your page is, and it will return the favour by quoting you correctly.— The Aureole Practice —
Honest questions
about structured data.
Schema attracts more myth than most disciplines — partly because it is invisible, partly because the tooling has changed quickly. If a question is missing here, the contact link at the foot of the page goes straight to the person who would answer it.
i What is schema markup, and why does it matter for AI search?
FAQPage with specific questions and answers. We implement Organization, LocalBusiness, Service, FAQ, HowTo, Product, Person, and other relevant types so that models have unambiguous, machine-readable information about your business. This structured layer is how you move from being passively indexed to being actively understood — and citable. Our parent AI Search Optimisation service sits directly on top of this work.ii Will adding schema directly improve our rankings?
iii JSON-LD, microdata, or RDFa — which format do you use?
iv Can you fix the schema produced by our SEO plugin?
@id URIs across pages, incomplete Organization declarations, and no FAQ or HowTo coverage on the pages that would benefit most. We audit what the plugin produces, configure it correctly, layer in the additional types it cannot generate (typically through theme-level JSON-LD blocks), and validate the combined output as one coherent graph. We do not rip out a working plugin; we make it earn its keep.v How long does a schema engagement typically take?
Organization, LocalBusiness, Person, primary Service markup. Weeks three and four cover content-specific schema, validation, and Search Console verification. Larger sites, e-commerce catalogues, and multi-location organisations can run longer; a focused single-type engagement (Product schema only, or FAQ rollout only) can run shorter. We scope honestly upfront based on the audit findings — and keep the engagement open as a small retainer once the build is done, because schema needs ongoing care.vi Do AI models actually read structured data, or only Google?
Where schema fits
in the whole.
Structured data is the foundation that the rest of AI search optimisation is built on. The link below returns to the parent service; the pills extend laterally to the sister sub-disciplines that compound with schema work.
Parent service
Sister sub-disciplines
Adjacent services
Ready to be read
correctly?
Start with a free SEO report — or reach out to scope a focused schema engagement. Either way, you’ll hear back from the team that does the work, not a sales department.