What Co mentions Reveal About the AI Recommendation Gap

What brands did we test and how did we test them?

For this case study, we tested 12 athleisure and activewear brands across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews: 14,140 API runs over seven days, using UK geography with web search enabled. For each brand, we ran two. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals. A useful companion note is New Study Finds 4 Key SEO Insights, because it looks at a nearby part of the same system.

The useful check is whether this improves the system behind search performance, not only the words on the page. Internal links, crawlable content, clear entities, current evidence, and a sensible page structure all help the recommendation become easier to trust.

Nike, the hero: Same KG description, completely different results

Nike, New Balance, and Reebok share the same KG entity description: "Footwear company." LLM probing across all five systems assigns all three unanimously to the athletic_footwear category, so from a pure entity clarity standpoint, they. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

The risk is usually hidden in the execution layer. A page can look fine to a human and still fail for an automated visitor if the form, call to action, rendering path, or confirmation step is not accessible enough for the agent to complete the task.

The third party citation weight in recommendation vs. recognition data

When we split citations by prompt type, recognition vs. recommendation, a pattern emerges that should reframe where most GEO budgets are being spent. For recognition prompts, where the user has already typed your brand name, own brand. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals. This connects with AI Recommendation Sets Leave Some Brands Out when the same signal needs a clearer operating decision.

Editorial roundups and comparison pieces

Being included in "best of" lists that name your category competitors is worth more to your concept graph than a standalone brand profile. The cluster signal comes from appearing in the same article as the brands that define the category. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

The practical value is in connecting the idea to an observable signal. That means deciding what should be checked, what would prove the issue is real, and where the team should make the smallest useful improvement first.

What brands did we test and how did we test them?

Recommendations: What the co mention data showed

We mapped how often brands appeared together in athleisure content across external sources (articles, reviews, comparison pieces, and editorial lists) crawled via API from UK indexed sources. Some of the most interesting results include:. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals. The same pattern also shows up in Brand Signals Are Rewriting the Authority Stack, where the practical question is how the signal becomes visible.

Nike, the hero: Same KG description, completely different results

The third party citation weight in recommendation vs. recognition data

What the co mention structure means for PR and content strategy

As we've seen so far, being mentioned in a category isn't enough. Being mentioned alongside the right brands in a category is what places you in the concept graph for that cluster. A press mention that describes a brand as "performance. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.