Type selection
Choose structured-data types that fit the page subject, visible information, and relationships the business can substantiate.
Schema Markup · Structured data
Schema Markup for AI Optimization
Structured-data implementation that represents a service business, its services, people, locations, and eligible page relationships accurately. Schema can improve machine-readable context, but it cannot directly produce inclusion in ChatGPT, AI Overviews, or any other answer.
What it is
Make eligible information machine-readable.
Schema markup is structured data, commonly expressed as JSON-LD, that describes entities and relationships already supported by the visible page. The implementation should match the business's actual organization, services, people, locations, and page content rather than add unsupported properties.
What we implement
Entity relationships
Represent the relevant organization, service, person, location, and page relationships without creating unsupported connections.
Validation and correction
Inspect syntax, required properties, duplicate entities, conflicts, and alignment between markup and the visible page.
Maintenance guidance
Document review points for changes to services, people, locations, relationships, or other underlying business facts.
Who it's for
Businesses whose structured data needs a careful review.
This service fits businesses with important entity, service, credential, or location information that needs to be represented accurately in machine-readable form. The visible page remains the source of truth; schema does not replace clear content or independent evidence.
Scope and limits
Structured context is not an inclusion mechanism.
Schema markup cannot guarantee ChatGPT or AI Overview inclusion, citations, recommendations, rankings, or business outcomes. Validation confirms that the implementation is readable and aligned with the reviewed page; it does not control how an external system uses that information. For a broader visibility diagnosis, see the AI Visibility Audit. For page clarity work, see Content Optimization for AEO.
Proof
Validated against the page, every time.
Every implementation is delivered with validation evidence — syntax checks, required properties, and confirmation that the markup matches the visible page — so the work is verifiable rather than asserted. Attributed examples of structured-data work within a broader engagement appear on our AEO results page as clients confirm them. A free Gap Check can review which pages and entities need attention first.
Questions buyers ask
Frequently asked questions
Which types of schema markup does our website need?
The needed types depend on the pages, entities, relationships, and visible content on the website. Common candidates can describe the organization, services, people, locations, breadcrumbs, and eligible questions, but we only recommend types that the page can support accurately. The page's actual subject controls the recommendation.
Can schema markup directly make our brand appear in ChatGPT or AI Overviews?
No. Schema markup cannot directly guarantee inclusion in ChatGPT or AI Overviews. It provides structured context that may help systems interpret a page, but inclusion depends on factors outside the markup and must be supported by the visible, accurate content. The markup is one interpretive signal, not an inclusion mechanism.
How do you determine which schema types belong on each page?
We review each page's purpose, visible claims, entities, relationships, and existing structured data before selecting types. The implementation should represent what users can see and what the business can substantiate, rather than adding types only because they are available. This keeps the structured data aligned with the page's actual subject.
Do you implement schema markup or only provide recommendations?
We can implement schema markup or provide recommendations, depending on the agreed scope. Implementation includes placing the structured data in the appropriate page context and documenting assumptions; recommendations describe the changes for the site's team to make. The selected approach depends on access, ownership, and the implementation boundary.
How do you validate schema after implementation?
We validate the generated structured data against the page content, inspect syntax and required properties, and check for conflicting or duplicated entities. Validation confirms that the markup is readable and internally consistent; it does not guarantee a rich result or AI inclusion. Any validation result must still be read alongside the visible page.
Can you fix incorrect or conflicting schema already on our website?
Yes. We can review incorrect, duplicated, or conflicting markup and recommend or implement corrections within scope. The correction should align the structured data with the current page, entity relationships, and visible claims rather than preserve unsupported properties. Unsupported or obsolete markup is removed rather than hidden by adding more types.
How is entity optimization different from basic structured data?
Basic structured data describes a page or entity in a machine-readable format. Entity optimization also considers how the organization, people, services, locations, and third-party references relate to one another across the site. Schema can express those relationships, but it cannot create evidence that does not exist. The goal is a coherent entity model supported by accurate information.
Does schema markup require ongoing maintenance?
Schema markup may need maintenance when pages, services, people, locations, or relationships change. We can document the implementation and identify review points within the agreed scope; maintenance is not a promise that every future site change will be detected automatically. A review is appropriate when the site's underlying facts change.
Find out what structured data can accurately represent.
Use a Gap Check to discuss the pages, entities, and relationships that need review.