AI Data Layer for Publishers

The sites AI cites aren’t luckier than yours. They’re more structured.

Authority sites and niche publishers have spent years building topical depth that Google rewards. That same depth is exactly what AI search engines want to cite, but only if they can read it. AI Data Layer turns your content archive into a structured, machine-readable knowledge layer that AI engines can query, attribute and cite.

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Joins thousands of WordPress sites already AI-readable.

The Problem

Google’s helpful content updates have already tested your traffic resilience. AI search is the next filter, and it’s a different kind of test. AI engines don’t reward quantity or domain authority directly. They reward structured, attributable, entity-rich content. A newer site with clean entity extraction can outperform a ten-year-old authority site that’s still serving unstructured HTML. Structure is the new authority signal.

Publishers with structured content registries are cited 4× more often in AI-generated answers than those without.

How AI Data Layer helps

Built for how you work

Turn topical authority into AI citations

AI Data Layer maps every entity across your content archive, the people, products, organisations and concepts your site covers, and publishes them as a connected, structured registry. That’s the topical depth AI engines use to establish citation confidence.

Compound value as you publish

Every new post adds entities to the registry. Over time, your site builds a knowledge graph specific to your niche, something a thin-content competitor can’t replicate quickly. The registry compounds; raw content doesn’t.

Wikidata-verified entity grounding

Entities extracted from your content are matched against Wikidata QIDs, globally recognised identifiers used by Google, Wikipedia and AI knowledge bases. Your content gets attributed to verified real-world entities, not ambiguous strings.

The features that matter here

Curated for your use case

Site-wide knowledge graph

A connected registry of every entity across your archive, the topical depth AI engines reward with citations.

Wikidata-grounded entities

Extracted entities matched to globally recognised QIDs, so your content attributes to verified subjects, not strings.

Verified JSON-LD structured data

Rich, fact-checked structured data added to your pages, the signal Google AI Overviews and assistants trust.

Pricing

The right plan for publishers

Early-adopter pricing

Growth

£24/mo

3 sites · built for large archives

Publishers with large archives should plan an initial bulk analysis sprint. Need 10 sites? Pro is £45/mo. Contact us for a higher one-time analysis allowance.

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Frequently asked questions

Prioritise by traffic and topical relevance. AI Data Layer lets you analyse posts individually or in bulk. Start with your top 100 posts by traffic, these are the ones most likely to be cited, then work through the rest systematically using your monthly allowance.

Yes. The NLP extraction works on any English-language content. Wikidata grounding works where entities have QID entries, which covers most established topics. For highly specialist niches, extracted entities that don’t match Wikidata QIDs are still included in your registry, just without grounding metadata.

Yes. Google AI Overviews draw from structured content signals including schema markup and entity clarity. AI Data Layer improves both, the JSON-LD schema it generates and the entity registry it maintains both contribute to how Google’s AI layer reads your content.

Indirectly. Wikidata-grounded entities establish that your content is about verifiable, real-world subjects, which contributes to topical authority signals. It doesn’t replace demonstrated expertise or author credentials, but it strengthens the structural layer those signals sit on.

Make your archive work harder for AI search

Install in one click and make your WordPress content visible to the engines your readers are already asking.

Install Free, No account required