Structured data that speaks AI's language.

SUBJCT automatically generates entity-aware JSON-LD structured data for every page in your content archive, then links each entity to its Google Knowledge Graph entry. This gives AI search engines and traditional search engines a machine-readable layer to understand what your content is about, without relying solely on parsing natural language.

Inside the editor, select the Schema tab and hit Update Schema. SUBJCT builds the JSON-LD in the background, based on the page template you're using, so the markup matches the content type automatically. Across an archive, the same generation runs via API at scale.

SUBJCT assigns schema type contextually per page, including Article, WebPage, FAQPage, HowTo and Product. The type is selected based on the template and content, it isn't applied as a single blanket schema across every page.

SUBJCT identifies the six to eight most important entities in an article and embeds them in the JSON-LD, with each one linked to its Google Knowledge Graph identifier. This resolves ambiguous names and brands into entities an AI engine can recognise with confidence, rather than strings it has to interpret.

Schema's proven role is helping engines understand and disambiguate content, not directly increasing citations. Current published evidence doesn't show a reliable causal link between adding schema and AI citation uplift. Its value is highest for entities a system doesn't already recognise with confidence.

Yes. SUBJCT includes one-click validation: select Validate Schema and the snippet opens directly in the Schema.org Validator, so you can confirm the markup is clean before publishing.

Both. Schema can be generated and reviewed per page inside the editor, or applied across an entire archive via API, built for publisher-scale volumes rather than a page-by-page workflow.

No. SUBJCT generates the schema for you, so there's no manual coding required. You can still review, edit, or validate the output inside the editor before it's published.