The entity layer: why AI gets your brand wrong
Models do not read brands as strings — they resolve them as entities. A practical guide to building an entity layer that keeps AI answers accurate.
Ask ChatGPT about your company and you will sometimes get a description that is subtly wrong. Outdated pricing. A discontinued product line. A feature you never shipped. A founder who left two years ago.
This is not the model being careless. It is the model doing what it was trained to do: resolving an entity from many sources and synthesizing the most probable description.
If the web says six different things about you, the answer will be an average of those six things. The fix is not PR. It is an entity layer.
What the entity layer is
The entity layer is the set of canonical facts about your business, published consistently and machine-readably across every surface a model might retrieve.
It includes:
- Identity — legal name, common name, domain, founding date, headquarters.
- Category — what you are, phrased the way the market phrases it.
- Offer — products, services, pricing model, target customer.
- People — named leadership and experts, with verifiable profiles.
- Claims — the numbers, capabilities and differentiators you are willing to stand behind.
Every one of these should be identical everywhere. Not similar. Identical.
Why consistency beats cleverness
Models retrieve from a small set of sources: your site, review aggregators, directories, press, LinkedIn, Reddit, and a handful of category publications.
If your homepage says “platform”, G2 says “software”, Crunchbase says “services company” and your LinkedIn says “agency”, the model must choose — and it will favor whichever sources it retrieves most, not whichever is most flattering.
Consistency gives the model a stable fact to repeat. That is the whole game.
Building it in five steps
1. Write the fact sheet
One page. Name, one-sentence description, three-sentence description, category, ideal customer, pricing model, headquarters, founding year, leadership. Written to be repeated, not admired.
This single document resolves 80% of entity ambiguity if it is propagated.
2. Fix your own properties first
Homepage, about page, product pages, author pages. Use the exact category language from the fact sheet. Add Organization, Product, Service and Person schema. Link authors to real external profiles.
3. Reconcile the directories
G2, Capterra, Crunchbase, LinkedIn, Wikipedia (if eligible), industry associations, chamber listings. These are the sources models retrieve most often. Update them, and put a recurring calendar reminder on it — directories drift.
4. Attach people to claims
Content published under a company byline is weaker than content published under a named expert with credentials and a profile. This is one of the few E-E-A-T signals that translates directly to retrieval: models need to know who is asserting something before they will cite it.
5. Monitor what gets said back
Sample 20–50 buyer prompts monthly. When an answer contains a wrong fact, trace which retrieved source it came from and fix the source — not the prompt.
Common mistakes
Treating schema as a switch. Markup reduces ambiguity; it does not guarantee citation. Fix the facts first.
Over-optimizing for one engine. ChatGPT, Perplexity and Gemini retrieve differently. Check all three.
Chasing new profiles. Adding a fifth unverified directory does more harm than good. Depth beats breadth.
Letting the fact sheet rot. An entity layer is a maintenance commitment, not a launch deliverable.
How you know it worked
Two signals:
- Description stability. Ask ten times over a month; the description converges instead of drifting.
- Correction latency. When you change a fact, how long until the answers change? A healthy entity layer moves that from months to weeks.
Neither signal is glamorous. Both are what separate brands that are cited confidently from brands that are described approximately.