Structured data is how machines learn what your company is
Structured data turns the information on your pages into a graph that machines can read one way only. Rich result eligibility is one output of that work; the larger gain is that search and generative engines read what your company is instead of guessing. This service builds the graph, validates it and protects it from breaking.
Who actually writes the Schema.org markup?
On most corporate projects, nobody does. The marketing agency stops at content; the software agency never touches the graph because it was not in the statement of work. The site goes live and the structured data is never built. We put that line in the contract: schema architecture, validation and regression control belong to one accountable party.
| Responsibility | Marketing agency | Software agency | Baki Bilisim |
|---|---|---|---|
| Who chooses the schema type that fits each page template? | Out of scope | Out of scope | In scope |
| Who designs the entity relationships and the @id graph? | Out of scope | Partly | In scope |
| Who verifies the graph still validates after a content update? | Out of scope | Out of scope | In scope |
The table describes categories of supplier, not named competitors. The full split, and why we hold it under one contract: why we publish measurements instead of claims.
What exactly is lost without structured data?
Without structured data, engines infer your company from page text. While inferring, they can confuse your name, address, services and the relationships between your pages; eligibility for rich results never forms at all; and generative engines cannot separate which fact belongs to you. The loss shows up in recognition rather than in ranking position.
Three concrete losses
- Entity ambiguity. You are merged with similarly named firms, with a former trading name, or with a branch in another city. To the engine you are not one entity but a pile of unconnected pages.
- Eligibility never forms. For rich result formats such as question sections, step-by-step instructions, product data and business information, the page never enters the candidate list. That is a registration problem, not a ranking problem.
- Attribution drifts. A generative engine takes the fact from you and credits the source to someone else. When no node defines the company, there is no entity to attribute the answer to.
Structured data
A method of labelling the information on a page with a standard vocabulary (schema.org) so that search engines and language models read it one way only. On corporate sites it is applied as JSON-LD: a graph embedded in the page source.
Why it matters: it defines the layer machines read without changing a word of what your visitors see. It is the foundation that AEO and GEO work is built on.
2011
The year the schema.org vocabulary was launched jointly by Bing, Google and Yahoo!; Yandex joined in the same year.
Source: schema.org — About · Accessed: 2026-07-29 · schema.org/docs/about (opens in a new tab) ↗
3
The number of structured data formats Google supports: JSON-LD, Microdata and RDFa. JSON-LD is the recommended format.
Source: Google Search Central — Introduction to structured data · Accessed: 2026-07-29 · developers.google.com (opens in a new tab) ↗
What exactly does this service deliver?
Nine deliverables: a current graph inventory, an entity map, the @id architecture, a page-type mapping matrix, template-level JSON-LD implementation, multi-location and multilingual graph structures, a validation report, a pre-publish regression check and a handover document. The format and delivery week of every item is written into the ledger below.
| # | Deliverable | Format | When |
|---|---|---|---|
| 01 | Current graph inventory and error dump — every page type listed, every existing node validated | Table + validator output | Week 1 |
| 02 | Entity map — company, legal entities, locations, people, services, products and their relationships | Diagram + relationship table | Weeks 1–2 |
| 03 | @id architecture and graph design — one permanent identifier per entity, canonical form fixed | Technical document | Week 2 |
| 04 | Page type to schema mapping matrix — which template emits which node | Matrix table | Week 2 |
| 05 | Template-level JSON-LD implementation — markup written into templates, not into individual pages | Template files (code) | Weeks 3–4 |
| 06 | Multi-location and multilingual graph structure — LocalBusiness per location, language versions consistent with hreflang | Code + document | Week 4 |
| 07 | Validation report — sampled per page type, with tool and date beside each result | Report (Validator + Rich Results Test) | Weeks 4–5 |
| 08 | Pre-publish regression check — a gate that blocks releases carrying a broken graph | Check script + checklist | Week 5 |
| 09 | Handover document and content team briefing — which field feeds which node | Document + live session | Weeks 5–6 |
Why is an @id graph different from pasting schema onto pages?
Markup added page by page describes the same company again on every page, and slightly differently each time: the trading name is abbreviated in one place, the address is incomplete in another, the phone number is formatted a third way. In an @id architecture each entity is defined once, given a permanent identifier, and merely referenced from every other page. The result is one company, one address, one identity — no matter how large the site grows.
The corporate graph core
This block stays identical on every page. It is the single definition of the company: it is referenced, never rewritten.
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "Organization",
"@id": "https://example.test/#organization",
"name": "Example Metal Industries Inc.",
"url": "https://example.test/",
"address": {
"@type": "PostalAddress",
"streetAddress": "OIZ 1, 3rd Street No 12",
"addressLocality": "Gebze",
"addressRegion": "Kocaeli",
"addressCountry": "TR"
}
},
{
"@type": "WebSite",
"@id": "https://example.test/#website",
"url": "https://example.test/",
"publisher": { "@id": "https://example.test/#organization" },
"inLanguage": "en"
}
]
}Page-type nodes
Each template adds only its own node and references the core. Because the company data is never written a second time, an update happens in one place.
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "Service",
"@id": "https://example.test/services/laser-cutting/#service",
"name": "Laser sheet cutting",
"serviceType": "Metal processing",
"provider": { "@id": "https://example.test/#organization" },
"areaServed": { "@type": "Country", "name": "TR" }
},
{
"@type": "WebPage",
"@id": "https://example.test/services/laser-cutting/#webpage",
"url": "https://example.test/services/laser-cutting/",
"isPartOf": { "@id": "https://example.test/#website" },
"about": { "@id": "https://example.test/#organization" },
"dateModified": "2026-07-29"
}
]
}The JSON-LD block in this page's own source was produced by the same method; view the source in your browser and paste it into a validator to check it yourself.
What are the stages of a schema engagement?
Six stages: inventory, entity map, graph design, template implementation, validation and regression control. Each stage has its deliverable, acceptance measure and accountable party written down before it starts, and no stage closes on a statement — it closes on a verifiable output. Typical duration is three to six weeks, depending on scope.
-
Inventory and current graph dump
Every published page type is listed, the existing structured data is collected and run through a validator. The output states which node sits on which page, where it fails and which page types carry no graph at all.
-
Entity map
The company, its legal entities, locations, people, services and products are related to each other on a single map. This is where it is decided which entity is modelled with which type, and which external records — corporate profiles, official registries — are linked as sameAs.
-
@id architecture and graph design
Every entity is given a permanent, unique @id, and repeated information is referenced rather than copied. The canonical form — protocol, host, trailing slash — is fixed for the whole site, so the same entity never exists twice under two identities.
-
Page-type mapping and template implementation
A matrix defines which nodes each template emits, and the JSON-LD is written into the template rather than into individual pages. Multi-location structures get a LocalBusiness node per location; multilingual structures get language versions kept consistent with hreflang.
-
Validation
The Schema.org Validator, the Google Rich Results Test and Search Console enhancement reports are run together. A sample is taken from every page type, and errors and warnings are closed in the template that produced them rather than on a single page.
-
Regression control and handover
The real risk is not the first build but every content update after it. A pre-publish check blocks releases carrying a broken or incomplete graph, and the content team is handed a written map of which field feeds which node.
These six stages run inside the wider engagement described in our process, contract and exit terms; no separate governance rhythm is introduced.
Which criteria must be met before the work counts as delivered?
Seven criteria: zero validator errors, a valid item on every template, one unique @id per entity, an exact match between schema and visible content, full coverage of required properties, no critical errors in Search Console enhancement reports, and a regression check that blocks releases carrying a broken graph. Thresholds and tools are in the table.
| Criterion | Threshold | Measurement tool |
|---|---|---|
| Validator error count | 0 | Schema.org Validator |
| Valid item of the targeted type | At least 1 on every template | Google Rich Results Test |
| @id uniqueness and consistency | One @id per entity, identical form on every page | Graph audit script |
| Schema to visible content match | Exact for name, address, telephone and dates | Sampled manual check + script |
| Required property coverage | Full coverage of the target type's required properties | Google Rich Results Test |
| Post-launch enhancement report | 0 critical errors after 28 days | Google Search Console |
| Regression control | Releases published with a broken graph: 0 | Pre-publish check step |
We do not undertake that a rich result will be displayed, and we do not promise a ranking position. Valid markup is our responsibility; the decision to display belongs to the search engine. What the contract commits to is eligibility, measured with named tools on named dates.
Every number we publish about our own site carries its tool and its measurement date: this site's measured scores.
Where does structured data make the biggest difference?
In structures where the same information repeats across many pages: multi-location networks, multilingual catalogue sites, manufacturers with wide product families and holdings that contain several legal entities. On a five-page site the gain is limited; across a network of hundreds of location pages it is decisive.
- 01 Manufacturing and Factories When product families, specification tables and capacity data are modelled with Product and ItemList nodes, a procurement search matches the whole catalogue rather than one lucky page.
- 02 Retail Chains A separate LocalBusiness node with opening hours for each store makes the store finder readable in local search, and the site, the app and the business profiles all read the same data.
- 03 Franchise Networks Defining hundreds of franchise locations consistently under one head-office Organization node prevents NAP inconsistency at source instead of correcting it directory by directory.
- 04 Export Industry A graph kept consistent with the correct inLanguage value and hreflang per language version puts the right version in front of the overseas buyer evaluating your company.
- 05 Corporate Holdings When the relationship between the holding, its subsidiaries and its brands is written with subOrganization and parentOrganization nodes, engines can tell which news item belongs to which company.
The seven questions we are asked most about structured data
What is structured data?
Structured data is a method of labelling the information on a page with a standard vocabulary (schema.org) so that search engines and language models read it one way only. On corporate sites it is applied as JSON-LD, a graph embedded in the page source. It does not change your visible content; it makes that content explicit for machines.
Which schema type should I use?
The type follows the page type: Organization for corporate identity, LocalBusiness for a site with a physical address, Service for service pages, Product for products, FAQPage for question sections, HowTo for step-by-step instructions, and Article or TechArticle for guides. Core nodes that repeat across the site are defined once and called from every other page by @id.
Does schema affect rankings directly?
Google does not describe structured data as a direct ranking factor; it uses it to understand content and to generate rich results. The indirect effect is measurable: a correctly built graph makes clear which question a page answers, establishes eligibility for rich result formats, and makes it easier for generative engines to describe the company accurately when they quote it.
Is a rich result guaranteed?
No. Valid structured data is the eligibility condition for a rich result; the decision to display one belongs to the search engine. That is why the contract commits to eligibility rather than display: zero validator errors, a valid item of the targeted type on every template, and no critical errors in Search Console enhancement reports.
How are schema errors found and fixed?
Three layers are used: the Schema.org Validator reports syntax and type errors, the Google Rich Results Test reports rich result eligibility, and Search Console enhancement reports aggregate errors across every published page. Fixes are applied at template level, not page by page, so an error closed in one template closes on every page that uses it.
Is plugin-generated schema enough?
On small sites it is a workable start; on corporate structures it falls short at three points. Plugins usually emit disconnected nodes that are never joined into an @id graph, they cannot model multi-location or multilingual structures, and they break silently when content is updated. Auditing and taking over the existing plugin output is also an option; rewriting from scratch is not compulsory.
Do AI engines read schema?
Generative engines process the HTML source of a page, JSON-LD blocks included, but none of them guarantees a citation because schema is present. The function of structured data here is entity clarity: when company name, address, founder, services and page relationships are defined once and consistently, the model reads the company instead of guessing it. We measure this alongside AEO and GEO work.
Questions about process, pricing, contracts and international invoicing are answered on the general FAQ page.
See the measured state of your digital assets within five working days.
The audit is free and creates no obligation to work with us. The report itemises AEO answerability, AI crawler access, Core Web Vitals, structured data validity and WCAG 2.1 AA gaps.
We work with corporate-scale, multi-location or multilingual organisations. One-off small jobs fall outside our scope; in that case we point you to smaller studios.