Most brands approaching AI visibility ask the wrong question: How do we get stronger as an entity so that LLMs recommend us more?
In entity SEO, we tend to say, “Build the Knowledge Graph, add schema, and get more press.” But that logic assumes the LLM is evaluating the brand and deciding whether it’s good enough to recommend for any query related to what the brand sells. The LLM evaluates the query and matches it against whatever category associations it has built for the brand from third-party content.
The difference matters enormously in practice.
As we’ve seen in multiple scenarios, recognition isn’t the same as recommendation. So being a recognized brand isn’t synonymous with being a strong brand.
What matters is whether the category your customers are using to search for you matches the category the LLM has coded you into.
What the data showed
João da Silva and I conducted a study of 12 athletic apparel brands in the U.K. over seven days, with 14,140 API runs across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews. We tested the same brands using two different category framings: athleisure and athletic footwear.
After looking at the results derived from co-mentions and putting numbers on the impact of framing on category recognition for LLMs, we took the test one step further and changed the category register in the prompt.
The results were symmetric to a degree that rules out noise:
| Brand | Knowledge Graph (KG) score | Athleisure rate | Footwear rate | Δ | Verdict |
|---|---|---|---|---|---|
| New Balance | 64,235 | 1% | 90% | +89 | Jumped (footwear-coded) |
| Nike | 25,996 | 77% | 90% | +13 | Small shift (footwear-coded with strong athleisure co-mentions) |
| Alo Yoga | 3,062 | 63% | 0% | -63 | Dropped (athleisure-coded) |
| lululemon | 810 | 90% | 0% | -90 | Dropped (athleisure-coded) |
| Sweaty Betty | 751 | 9% | 0% | -9 | Stable |
| Reebok | 665 | 1% | 20% | +19 | Small shift |
| Outdoor Voices | 455 | 26% | 0% | -26 | Small shift |
| Rhone Apparel | 400 | 5% | 0% | -5 | Stable |
| Varley | 381 | 6% | 0% | -6 | Stable |
| TALA | 356 | 5% | 0% | -5 | Stable |
| Gymshark | 277 | 37% | 0% | -37 | Dropped (athleisure-coded) |
| LNDR | 2 | 0% | 0% | 0 | Stable |
Notes:
- New Balance goes from 1% to 90%.
- lululemon goes from 90% to 0%.
The variation is approximately 0.9 points in both directions simultaneously.
We’re not talking about correlation here, but a controlled observation: what happens when we change only one variable — the category word in the prompt.
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Why this happens: Category coding
Nike, New Balance, and Reebok share the exact same Google Knowledge Graph (KG) description: “Footwear company,” so all three are recognized perfectly by every LLM we tested. From an entity standpoint (recognition), they start from an identical position. However, their behavior under different category framings isn’t identical at all.
The reason is what the paper…
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