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Entity Clarity: Making Sure AI Knows What You Actually Are

When an assistant describes you as the wrong kind of company, this is usually why — and it's the cheapest thing to fix.

entitiesconsistencyaeo

You asked an assistant what your company does, and it answered confidently and wrongly. It put you in a category you left two years ago, or described you as an agency when you sell software, or attached you to a market you have never sold into. Nobody attacked you. Nothing was hacked. The description you are seeing is an average of the descriptions available about you, and most of those descriptions are yours.

This is the cheapest problem on the list of things that affect how assistants talk about your brand, because you can fix most of it with an afternoon of editing and a few emails. It is also the one most teams skip, because it involves no new content and produces no immediately satisfying artifact.

What an entity is, in practical terms

Forget the knowledge-graph diagrams for a moment. In practical terms, an entity is the bundle of statements a system holds about a named thing: what it is called, what type of thing it is, what properties it has, and what other things it is connected to.

Assistants assemble descriptions of your company from text they were trained on and, in many products, from documents retrieved at answer time. Neither path gives you a settings panel. What you control is the supply of statements. If the statements about you agree, the description that comes out the other end is more likely to be the one you would write yourself. If they disagree, something has to break the tie, and you do not get a vote on how.

Four things carry most of the weight:

  • Name. The exact string, including legal suffix, spacing, and casing.
  • Category. The noun phrase that says what type of organisation or product you are.
  • Attributes. Who you serve, what you sell, where you operate, how you are delivered, what you cost in model terms.
  • Relationships. Parent company, subsidiaries, former names, integrations, founders, notable products.

A common failure is having all four correct on your homepage and contradicted on eight other properties you also own.

The consistency audit: name, category, attributes, relationships

Build one table. One row per property where a description of your company exists, one column per field. Fill it in by copying the actual live text, not the text you believe is there.

PropertyName string usedCategory nounPrimary audience statedFounded / HQRelationships statedLast edited by
Homepage
About page
Product page
Structured data (Organization)
Social profile bios
App or marketplace listings
Job postings
Press release boilerplate
Partner and integration directories
Review and directory profiles
Public reference works
Conference and podcast bios

Then add two derived columns you fill in last: agrees with canonical? (Y/N) and who can change it. That second column is the one that turns an audit into a plan, because it splits the work into edits you can make today and requests you have to send to someone else.

The canonical row is not one of the rows in the table. Write it separately, once, as a short block: the exact name, one category noun phrase, three to five attributes, and the relationships that are true today. Everything else is measured against that block. If you cannot write the canonical block without an internal argument, you have found the real problem, and no amount of publishing will paper over it.

Where inconsistency usually creeps in

Inconsistency is rarely a mistake. It is usually the residue of decisions that were sensible at the time.

  • Repositioning. You changed what you sell. The homepage moved. The About page, the boilerplate, and the directory profiles did not.
  • Audience-specific copy. The enterprise page says one category, the self-serve page says another, and both are technically true.
  • Old names. A rebrand leaves the previous name attached to most of your historical coverage, which is often the coverage with the longest life.
  • Acquisitions and spinouts. Relationship statements go stale fastest and are the hardest for an outsider to verify.
  • Distributed publishing. Recruiters write company descriptions in job posts. Partner teams fill in directory forms. Nobody reviews either.
  • Aspirational category claims. Marketing describes the category you want to be in; every third party describes the category you are visibly in. The gap gets resolved against you.

Fixing your own properties first

Start here because it is fast, fully in your control, and because third parties frequently copy from you.

  1. Publish the canonical block on your About page as plain prose, not a graphic. Keep it short enough that a retrieval system lifting one paragraph gets a complete and correct statement.
  2. Repeat the category noun in the first sentence of your homepage, About page, and product pages. This is not keyword repetition. It is making sure that any single extracted section says what kind of company you are without needing the rest of the page. Our guide to chunk-friendly structure covers the general rule.
  3. Fix your structured data so the Organization markup matches the prose exactly: name, alternate name if you rebranded, description, sameAs links to profiles you actually control. Do not mark up a category you do not describe in the visible text.
  4. Update boilerplate everywhere it lives: the press kit, the email footer, the deck template, the job-post template. Boilerplate is the single highest-leverage file because other people copy it verbatim.
  5. Make the former name explicit if you have one. A sentence saying "formerly known as X" does more to resolve a conflicted entity than deleting every trace of the old name, which you cannot do anyway.
  6. Check your product pages carry the same attributes as your About page. Product pages built for retrieval go further on this.

Fixing the third-party sources that describe you

On-site consistency alone is weak evidence. It is self-description, it is uncontradicted only because nobody else has spoken, and a system weighing sources has an obvious reason to treat independent descriptions as more informative than your own marketing copy. Third-party agreement is what turns your claim into a fact the wider record supports.

That does not mean carpet-bombing every listing that mentions you. Mass-editing profiles you do not control is a bad idea on three counts: many platforms have their own editorial rules, bulk changes from an interested party are exactly what those rules exist to catch, and a burst of identical edits across the web is a pattern nobody benefits from being associated with.

A proportionate approach:

  • Rank the sources by how often they are actually referenced in the answers you care about, not by domain authority instinct. If you already run measurement, you have the citation list. If not, collect the sources from a handful of manual answer captures first.
  • Fix the ones you own or administer immediately: claimed profiles, partner directories where you filled in the form, app-store listings.
  • Request corrections on the rest, one at a time, with evidence. A short email pointing at a specific factual error and linking the canonical page works far better than a request to change positioning language.
  • Respect the editorial rules of public reference works. Where a source has a conflict-of-interest policy for subjects editing their own entries, follow it. Use the mechanism the platform provides, disclose who you are, and accept that you do not get the final word.
  • Leave opinion alone. You can correct "founded in 2019" or "acquired by X". You cannot correct "not as good as Y", and trying to is a different and worse problem.

If an assistant is not just miscategorising you but asserting something damaging and false, that is an incident rather than an audit. See when the model is wrong about your brand for the sequence that applies there.

Entity consistency removes a reason for a system to describe you incorrectly. It does not create a reason for anyone to recommend you. Treat it as clearing an obstacle, not as a growth tactic, and you will size the effort correctly.

How to verify the fix took, and when to expect it

Verification has to be defined before you start editing, or you will end up arguing from impressions.

  • Capture a baseline first. A fixed set of prompts, run across the models you care about, several times each, recorded with dates and model versions. Miscategorisation is easy to measure because the outcome is categorical: did the description name the right category, yes or no, across N runs.
  • Freeze the prompt set. If you change wording between runs, you cannot tell whether the description changed or the question did.
  • Re-run on a schedule, not on impulse. Checking daily will show you variance and convince you something happened when nothing did.
  • Record what you changed and when. Without a change log you have correlation with no timeline to hang it on.

On timing, be honest with your stakeholders up front: content retrieved at answer time and knowledge baked into a model move on very different clocks, and a change to your own site is visible to one of those paths much sooner than the other. We cover the two clocks and how to measure the lag in how long it takes to change an AI recommendation. Nobody, including us, can tell you a date. What you can do is measure the lag you actually observe and use your own number next time.

And keep the causal claim modest. If the description improves after a consistency pass, you have a plausible explanation and a documented change, not proof. Say that in the report. It survives scrutiny better than certainty does.

In Plain English

What this means: assistants describe your company using the statements available about it. When those statements disagree, you get a description you did not choose.

Who this helps: any team that has repositioned, rebranded, been acquired, or grown fast enough that nobody owns the company description any more.

When to use it: before you invest in anything more expensive on the what actually changes recommendations list. It is the cheapest lever and it makes every later change easier to interpret.

What to do next: write the canonical block, fill in the audit table, fix what you own, and send three correction requests to the sources that matter most. Capture a baseline before you touch anything.

Start with the audit table above, or pick up the full version in our resources and downloads library along with the AI-readiness audit checklist.

Primary-source verification

Checked July 28, 2026. The operational and factual guidance above was reviewed against the primary references below. Provider features, prices, policies, interfaces, jurisdictions, and account-specific results can change; recheck the controlling first-party document and your own account or measured data before acting.