# Entity grooming: make AI models agree on who you are

Ask a model about a brand it half-knows and watch what it does: it hedges. "X appears to be a software company that offers various solutions…" That vagueness is not a model failure. It is a data failure. The model found five different descriptions of you across the web, and rather than pick one, it averaged them into mush — or left you out of the answer entirely.

Fixing this is the least glamorous, highest-leverage work in [GEO](/resources/what-is-generative-engine-optimization). Call it entity grooming: making every machine-readable mention of your brand say the same thing.

## Consensus beats backlinks

The old currency was volume: more links, more authority. Entity resolution works differently. When a model (or Google's Knowledge Graph) decides what your company *is*, it looks for agreement across sources. Five identical, machine-readable descriptions of your brand do more for your entity than fifty backlinks that each describe you a little differently. Varied signals don't add up — they cancel out. Consistent signals compound.

That's the mechanism behind the hedging: identity disagreement reads as uncertainty, and models are trained not to assert what they're uncertain about. A model that isn't sure whether you're "a compliance platform" or "a healthcare AI company" won't confidently recommend you as either.

## The two artifacts

Entity grooming starts with writing two things and then refusing to improvise ever again.

**The 7-word handle.** One line, seven words or so, that says what you are: "Stripe: payments infrastructure for the internet." Not clever, not aspirational — a computational handle a model can grasp, store, and repeat. If five people can't repeat it back verbatim after hearing it once, it's too complex.

**The 50-word entity description.** One paragraph covering who you are, what you do, who it's for, and one differentiating fact. This is the version that goes everywhere a longer field exists.

## Deploy verbatim, everywhere

The discipline is in the deployment. The same words — not paraphrases — across:

- Your site's `Organization` schema and About page
- LinkedIn company description
- Crunchbase and G2 profiles (yes, the ones nobody has touched since the last funding round)
- Every partner directory and integration marketplace listing
- PR boilerplate, so press coverage inherits the canonical phrasing
- Wikipedia, if you have a page — and the sources it cites

Then do the outreach nobody enjoys: find the top handful of third-party pages that describe you in outdated or divergent terms, and ask them to update to the canonical description. Most sites want accurate information; many will simply say yes. Each updated high-authority mention teaches the next training run that the scattered old descriptions are wrong and this one is right.

## Anchor it with Wikidata and sameAs

Consensus in prose needs an anchor in data. Add a [`sameAs`](https://schema.org/sameAs) array to your `Organization` schema linking your LinkedIn, Crunchbase, Wikipedia, [Wikidata](https://www.wikidata.org/), and social profiles — an explicit identity graph saying "these are all the same entity." If you don't have a Wikidata entry, create one; it's the closest thing the web has to an entity registry, and grounding to it is what lets different models converge on the same "you." (This pairs directly with the JSON-LD work covered in [structured data: the schema that earns citations](/resources/structured-data-the-schema-that-earns-citations).)

## How you'll know it worked

The test is simple and slightly nerve-wracking: ask ChatGPT, Claude, and Gemini what your company does — without leading them. Groomed entities get described in something close to your canonical phrasing. Ungroomed ones get the hedge. Expect months, not days; you're waiting for crawls, retrieval indexes, and eventually training runs to catch up. This is exactly why [invisible brands stay invisible](/resources/why-your-brand-is-invisible-to-ai) — the fix is cheap, but the clock only starts when you ship it.

Entity and identity consensus is one of the eight dimensions the Legible Readiness Index scores. [Run a free report](/) to see whether the machines currently agree on who you are — and what to align first if they don't.