Your content, built to be found by AI search engines.

The SUBJCT Editor is a content workspace that optimises every article for AI search as you write or edit. It combines page structure templates, prompt optimisation, entity tagging, automated internal linking, and JSON-LD schema generation in a single interface so content is structured, tagged, linked, and machine-readable before it's published.

The editor is built for content marketers and technical SEOs who need to produce AI-search-ready content without handling each optimisation task manually. It's used by in-house content teams, content engineers, and SEO specialists transitioning from traditional search to AI search.

Enter a prompt into the editor and SUBJCT fans it out into nine related query variations. Apply those prompts to individual content blocks - headings and paragraphs - and SUBJCT returns a cosine similarity score showing how well each block aligns with that query. A score above 60 is a pass; above 80 is strong. Edit the block, re-run the analysis, and save. Your overall prompt optimisation score updates across every prompt tested.

Entity optimisation is the process of identifying and strengthening the named entities - organisations, people, products, places, events - present in a piece of content. AI search engines assess topical authority partly by the depth and consistency of entity coverage across a site. SUBJCT surfaces the top entities in your article and suggests related ones you haven't yet covered, so you can add them to improve the content's credibility signal to AI engines.

SUBJCT chunks your content into contextual segments, creates a vector embedding for each chunk, and finds the closest semantic matches in your ingested archive. It then generates anchor text and implements the link directly in the article. Article-to-article linking builds topic clusters across the archive; entity linking connects the first appearance of a named entity to its relevant topic or tag page. All links are editable in the editor, and candidate links  (matches found but not automatically applied) are available for manual review.

SUBJCT generates entity-aware JSON-LD structured data for each page, with the schema type assigned contextually based on your content template: Article, FAQ, WebPage, HowTo, or Product. The schema embeds the top six to eight entities identified in the article and links each to the Google Knowledge Graph, providing AI indexing systems with an unambiguous, machine-readable entity profile for the content.

Yes. The editor includes a one-click validation step that opens the schema.org validator. Copy the generated JSON-LD snippet, paste it into the validator, and confirm the output is clean before publishing. The same snippet can be run through Google's Rich Results Test to check eligibility for rich results.

The page structure templates, prompt optimisation, entity tagging, and schema generation features work from the moment you open the editor. Automated internal linking requires your archive to be ingested first, SUBJCT needs the existing content to find semantic matches and implement links. Archive ingestion is handled through the Configuration settings or via the API.

Yes. SUBJCT has a WordPress plugin that brings automated linking and schema generation directly into the CMS. The full editor and entity analysis features are available via the SUBJCT web application. For headless CMS architectures, SUBJCT is API-first and integrates into any content stack.

No. SUBJCT optimises the structure, machine readable data, and linking of content you've already written, the signals that determine whether that content is readable and citable by AI search engines. The editorial content itself remains unchanged. SUBJCT is built around human-in-the-loop content: it makes human-authored content perform better, not replace it.