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. 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
Organizationschema 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Then do the outreach nobody enjoys: find the top handful of third-party pages that describe you in outdated or divergent terms, and ask them to update to the canonical description.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 array to your Organization schema linking your LinkedIn, Crunchbase, Wikipedia, Wikidata, 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.)
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 — 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.