01Entity optimisation
Entity optimisation: being named is useless if you are described wrongly
Entity optimisation makes search engines and AI models resolve your brand correctly: who you are, what you sell, who you serve and how you relate to your products, people and markets. It is the fix for the most under-diagnosed problem in AI visibility.
Quick answer
Entity optimisation makes search engines and AI models resolve your brand correctly, who you are, what you sell, who you serve, and how you relate to your products, people and markets.
It is the fix for the most under-diagnosed problem in AI visibility: brands are more often described incorrectly than absent entirely, and misdescription costs more than absence.
Key takeaways
- Three failure states, and only one is invisibility. Absent, uncited, and named-but-wrong each need a different response.
- Stale third-party profiles are the most common cause, and the fastest to fix.
- Your own site often disagrees with itself on founding year, product tiers or leadership.
- Pronouns break entity association. We name things repeatedly on purpose.
- Corrections land at different speeds by platform. Weeks on Perplexity, considerably longer on Gemini.
- Knowledge panels are a side effect, not a deliverable. No agency controls them.
Definition
An entity is a thing a machine can identify and hold facts about: a company, a product, a person, a place. Entity optimisation is the work of making those facts consistent, connected and unambiguous everywhere a machine might read them.
Three failure modes, and only one is "invisibility"
When we run a baseline, brands fall into one of three states. They need completely different responses, and guessing between them wastes a quarter.
- Absent. The assistant does not mention you at all. A coverage and authority problem, handled through search work and the wider programme.
- Present but uncited. Your brand is named without a link to you. Often the model is drawing on a third-party source that describes you. Entity work plus source strategy.
- Present and wrong. The assistant confidently states your pricing, positioning, ownership, location or product scope incorrectly. This is an entity problem, and it is frequently the highest-value fix in the whole engagement: a wrong answer costs more than no answer.
Where wrong facts come from
| Source | What it looks like | Effort to fix |
|---|---|---|
| Stale third-party profile | A comparison site or directory quoting pricing from two years ago | Days. Usually the fastest win available |
| Your own site disagreeing with itself | Different founding years, office counts or product tiers across pages | Days, once you have a canonical fact set |
| Name collision | Another company or product shares your name and the model merges you | Weeks. Needs explicit disambiguation everywhere |
| Markup describing an aspiration | Schema claiming offices, awards or ratings not visible on the page | Days, and it should be removed rather than justified |
| The fact was never stated plainly | The model is wrong because the right answer exists nowhere extractable | Weeks. Write it down somewhere a machine can read it |
What the work involves
Fact audit
Assemble the canonical facts about your organisation, products and people. Then find every place a machine can read a contradicting version, on your site and off it. This document becomes the reference everything else is coded against.
Disambiguation
Where a name collision exists, make the distinguishing attributes explicit and consistent: sector, location, founding, category, key people. This matters more in some markets than others. It is unusually common in India.
Explicit relationships
State the connections rather than implying them. Which products belong to which brand, which people hold which roles, which markets you actually serve. Pronouns break entity association; we name things repeatedly on purpose.
Structured data that matches reality
Organization, Person and Product markup that agrees with the visible page. Validated, and never used to claim something that is not on screen. Handled with the technical workstream.
Third-party correction
Update the profiles and listings the models are actually drawing on. The audit tells us which ones those are, rather than us guessing at a directory list.
Re-sample and verify
Run the same prompts again after corrections propagate. Entity fixes are one of the few areas where cause and effect are relatively legible, which makes them useful early proof that a programme is working.
Why this moves faster on some platforms than others
Perplexity is retrieval-led, so corrections tend to surface within weeks. Gemini blends retrieval with model knowledge, so a fact absorbed during training can persist after your site is fixed. We set that expectation before starting rather than explaining it in month three.
A caution about knowledge panels
Entity work often improves how a brand appears in knowledge panels and similar surfaces. It is not a guarantee, and no agency controls those. We treat improvement there as a welcome side effect of getting the facts consistent, not as a deliverable we promise.
02Common questions
What buyers ask about entity work
What is entity optimisation?
Making search engines and AI models identify and describe your brand correctly and consistently. An entity is anything a machine can hold facts about: a company, product, person or place. The work is making those facts agree everywhere a machine might read them.
How do I know if I have an entity problem?
Ask an assistant to describe your company, your pricing and your product range. If it names you but gets facts wrong, that is an entity problem rather than a content or authority one. It is also usually the cheapest of the three to fix.
Where do wrong facts usually come from?
Stale third-party profiles, most often. Directory listings, review platforms and old press coverage carrying details from two funding rounds ago. Second most common is your own site disagreeing with itself across different pages.
Will this get me a knowledge panel?
Sometimes, as a side effect. We treat improvement there as welcome rather than promised, because no agency controls those surfaces. Anyone selling a guaranteed knowledge panel is selling something they cannot deliver.
How long do entity corrections take to appear?
Retrieval-led platforms such as Perplexity often reflect changes within weeks. Platforms leaning on model knowledge, like Gemini, take longer because the incorrect fact may have been absorbed during training rather than retrieved.
Does structured data fix this?
It helps, and it is not sufficient. Markup describes what is on your page; it cannot correct a third-party source the model is actually reading. Implement it properly, then go fix the sources the audit identifies.
Sources
Every figure on this page comes from one of the following. Where a claim is ours rather than a source’s, it carries an evidence label instead.
- Rankability — State of AI Search, 1,645 AI citation observations across 31 topics, 2026. Source for the 24.1% cross-platform overlap, the 73.5% ChatGPT exclusive citation rate, the 22.1% vs 8.7% first-100-words finding and the 43.3% vs 15.5% query-breadth finding. Independent study
- Google Search Central — Optimizing your website for generative AI features (published 15 May 2026, updated 10 July 2026). Source for the llms.txt, chunking and AI-schema positions, and for the statement that generative features run on core ranking systems. Primary
- Searlixa sampling across client engagements. Any figure drawn from this carries an Observed label with its sample size and date. Our own
Ask an assistant
Hand this page to an assistant and ask it to summarise or challenge the argument. If it gets something wrong about us, that is itself a useful finding.
→What you are buying
Entity optimisation services: what is included
Entity work is about accuracy rather than volume. Being described wrongly is worse than being absent.
- Canonical fact sheet assembled for your organisation, products and people
- Contradiction audit across your own site and the third-party sources describing you
- Organization and Product schema corrected to match what the page visibly shows
- Disambiguation programme where a name collision exists
- Re-sampling to verify every fix actually landed
Who this is for
Brands being described inaccurately by assistants, and brands sharing a name with something better known.
Who it is not for
Anyone who wants schema added for its own sake. No schema type showed a consistent citation advantage in 2026 research; this is about accuracy, not markup volume.
From $900 a month. Usually bought inside a wider engagement, because entity fixes need somewhere to land. SEO and AI visibility pricing is published in full.
The audit includes a contradiction check across your own site and the third-party sources that describe you. The $99 SEO and AI visibility audit comes back in three working days, and the fee comes off your first invoice if you continue.