Entity Optimisation
Entity optimisation is the work of making your brand, people, products or locations resolvable as distinct, unambiguous entities in the knowledge graphs and models that search engines and AI systems already reason with. Rather than chasing keywords, you make it unmistakable who you are, what you connect to, and why that information can be trusted enough to repeat.
What an entity actually is
An entity is a distinct real-world thing: a company, a person, a product, a place, a concept. The baker Müller in Cologne is not the singer Müller in Hamburg, even though the name string is identical. Search engines and AI systems reason over entities and the relationships between them, not just over text strings, so the underlying question is always: which Müller does the user mean? Entity optimisation supplies the signals that let a system answer that correctly. The more clearly an entity is described and tied to sources a model already trusts, the more confidently that system can use it in an answer without conflating it with something else.
Why this matters for AI visibility
AI assistants only recommend what they can confidently identify. ChatGPT alone reached 900 million weekly active users by late February 2026, Google's Gemini app passed 1 billion monthly active users in August 2026, and Google AI Overviews now reaches over 2 billion monthly users across 200-plus countries — if a system can't pin down what your brand does and where it operates, none of that traffic ever sees you named. This is also where entity work earns its keep over link-building alone: an Ahrefs analysis of roughly 75,000 brands found that how often a brand is mentioned across the web correlates with AI citation rate at about 0.664, close to three times the correlation for backlinks (about 0.218). Being talked about, consistently and by name, in places a model already reads is a stronger lever than acquiring links. Entity optimisation is what makes those mentions resolve to one clear "you" instead of scattering across ambiguous variants.
How to approach it in practice
Start with a single consistent description: name, activity, location and core offer should read identically on your site, in directories and across social profiles. Add structured data (Schema.org, typically as JSON-LD) — an Organization or LocalBusiness type, say — so the same facts are machine-readable, but treat it as a clarity aid rather than a ranking trick: Google's own guidance is explicit that no special schema, markup or AI-specific file is required for AI Overviews or AI Mode. Get referenced by sources a model is likely to already trust — press coverage, Wikipedia, industry directories, review sites — since third-party mentions do more work here than anything you publish about yourself. Build genuine topical depth so it's clear where your authority actually sits. Above all, stay contradiction-free: mismatched opening hours, company names or founding dates confuse a model and weaken the entity. Check periodically that every public-facing detail still agrees.
Common mistakes
The most common failure is inconsistency: the company name on the website doesn't match the one in the industry directory, the address drifts between listings, the logo varies. Every contradiction forces a system to guess, raising the odds your entity gets merged with someone else's. A second mistake is pure keyword thinking — stacking search terms while never clarifying who actually stands behind the brand leaves you fuzzy to any model trying to place you. A third, increasingly common one: pouring effort into an llms.txt file expecting it to move AI visibility. Google's John Mueller confirmed in 2025 that no Google Search system reads or acts on llms.txt, and an Ahrefs analysis of roughly 137,000 sites that published one found about 97% saw no measurable referral traffic from it. Claiming structured data that isn't actually reflected on the page is a separate risk again — that reads as manipulation and can be penalised. And finally, many treat entity work as a one-off project rather than upkeep: every change needs to propagate everywhere, or the entity goes stale and loses trust.
Example
Picture a small tax firm in Leipzig that almost never surfaces in AI answers. The reason: it's "Kanzlei Berger & Partner" on its own site, "Steuerberatung Berger" in the local directory, three different address formats scattered across the web, and nowhere does it clearly say it specialises in trades businesses. After entity optimisation, every listing uses the identical name, an Organization schema records location and specialism consistently, and its profiles link to one another instead of contradicting each other. Weeks later, an AI assistant names the firm in response to a question about tax advisors for tradespeople in the region — not because of a ranking trick, but because the system can finally tell, without guessing, exactly which Berger it's looking at.
Common questions
Is entity optimisation the same thing as classic SEO?
No. Classic SEO is built around keywords and ranking position in a results list. Entity optimisation is about making sure a system understands you as one clear, trustworthy unit rather than an ambiguous string of text. They complement each other, but for AI visibility specifically, unambiguous identity is the part that decides whether you get mentioned at all.
Do I need structured data to do this?
It helps, but it isn't required and it isn't a shortcut — Google has said explicitly that no special schema is needed for AI Overviews or AI Mode. Structured data like JSON-LD makes your facts machine-readable and reduces ambiguity, but a plain, contradiction-free description of your entity, repeated consistently everywhere you appear, already does most of the work.