What It Is
Entity engineering for AI visibility is the deliberate construction of a structured data architecture that gives large language models the consistent, corroborated facts they need to decide whether to cite you. Where traditional SEO chased rankings, this discipline chases citations: getting named as the source inside ChatGPT, Perplexity, and Google’s AI answers. You engineer your attributes so they appear identically across your own schema and independent third-party sources. LLMs weigh consensus, so the more places that agree on your facts, the more likely a model is to surface you.
How It Works
- Map the specific questions and topics you want to be cited for, then define the exact entity attributes an AI would need to answer them with you.
- Implement rich, nested schema (Organization, Person, Product, FAQ, HowTo) so your facts are explicit and machine-parseable rather than buried in prose.
- Seed the same attributes across independent corroborating sources — podcasts, directories, industry publications, Wikidata — so models see agreement.
- Structure on-page content in clear, extractable chunks (definitive answers, definitions, comparison tables) that LLMs can lift cleanly.
- Test visibility by prompting ChatGPT, Perplexity, and Google AI Overviews with your target questions, and log whether and how you are cited.
- Close gaps iteratively: where a model cites a competitor or gets a fact wrong, publish and corroborate the missing signal, then re-test.
Who Recommends It
- Jason Wade endorses — Founder of Ninja AI focused on structured data architecture for LLM citation. (episode link pending) Unscripted SEO Podcast →
Difficulty & Time Estimates
HardDifficulty
6–12 moTime to Results