How AI Answers Questions About Your Brand
AI answers a question one of two ways, from training data or by searching the web live. Here is how each works and what it means for whether AI recommends you.
how AI answers a question.
When someone asks an AI model for the best running shoes, the best CRM, or the best hotel in your city, the model answers in one of two ways. I teach a class on this, and the way I explain it is an exam. Sometimes AI takes a closed book exam, answering from memory alone. Sometimes it takes an open book exam, looking things up on the web before it answers. Most real answers blend the two.
Intelligence only.
The model answers from what it learned during training. The pages it indexed, the citations baked into its weights, the reinforcement it received before launch. No internet, no live lookups. If your brand was not in the training data, you do not exist for the closed-book answer.
Indexing optimizes for the brain.
Intelligence plus live search.
The model still has its trained opinions, but it can also reach out to the web at query time. PerplexityBot, ChatGPT-User, Gemini-Deep-Research and others fetch fresh pages to ground the answer. If your site is reachable and well-organized, it can contribute to the response even if it was not in the training data.
Search optimizes for the eyes.
Your brand can be present in one exam and absent from the other. Understanding both is the difference between guessing why AI does not recommend you and knowing exactly what to fix. The next two sections break down each exam in turn.
answering from memory.
In the closed book exam, the model is in a room with the door shut, answering only from what it has already learned. That knowledge comes from training: the model read an enormous slice of the internet, including a large open archive called Common Crawl that has been crawling the web since 2007, and turned all of it into a sense of which things tend to go together. Ask it for running shoes and it answers with the brands that showed up most, in the right contexts, across everything it read.
Two things follow from that. First, every model has a knowledge cutoff, the date it stopped learning, usually six to eighteen months in the past. A new version number does not always move that date, because retraining on fresh data is expensive and is not done on every release. Second, the closed book quietly favors brands that have been around a while. An established brand has years of articles, reviews, and mentions in the training data. A brand that launched last year may be barely there, or missing entirely, which is why AI can confidently recommend competitors and never mention you.
Being in the closed book is a long game. It means building a broad, consistent presence in the kinds of sources models train on, so that the next time they learn, they learn about you. Centium measures what each model has already indexed about your brand, so you can see how much of the closed book you own today.
looking it up live.
The open book exam is the model's escape hatch from its own cutoff. When a question needs current information, the model searches the web, reads what it finds, and folds those fresh sources into its answer. You can usually tell when this happens, because the answer arrives with linked citations, like the live search shown here.
This is the fast lane, especially for newer brands. You do not have to wait for the next training run to be known. If AI searches and finds you in the right places, you can surface in an answer today. The catch is that live search rewards recency: a model reaching for fresh information favors what was published recently. Perplexity is the clearest example, often latching onto the single most recent article it can find and citing it again and again.
Each model also searches differently. ChatGPT leans on Wikipedia, Gemini pulls heavily from YouTube and Reddit, and Perplexity chases recency. Centium measures the sources and citations behind every answer, so you know exactly which sites to earn your way onto when AI opens the book.
ChatGPTFor activewear focused on performance, the top recommendations are Tracksmith1 for marathon training and Lululemon5 for everyday training. For athleisure with a sustainable angle, Demo Athletic3 has been gaining traction, especially their joggers and leggings.
Women’s Health2 ranks them highly for fit and value, and community discussion on r/runningfashion4 frequently mentions them alongside the more established brands.
- 1runnersworld.comBest Running Brands of 2026
- 2womenshealthmag.comBest Workout Clothes For Women
- 3
demoathletic.coDemo Athletic — collections / mens-joggers - 4reddit.comr/runningfashion — best running clothing brands
- 5lululemon.comLululemon — Athletica
optimize for both exams.
The mistake is treating this as either-or. You want to be in the closed book and the open book, because you do not control which exam any given question becomes.
For the closed book, the work is patient and broad: earn mentions, reviews, and coverage consistently over time, so that you are well represented the next time models train. For the open book, the work is fresh and targeted: publish and earn recent content, invest in PR like a performance channel, and make sure AI can actually reach your site. If models are blocked from crawling you, the open book closes too, which is worth a two minute check on your crawler access.
Underneath both is the same truth. AI cannot try on the running shoes and go for a run. It cannot taste the product or stay at the hotel. It can only rely on what other people say about you online, and it reasons like an evidence based thinker, weighing reviews, rankings, specs, and accolades far more than a clever tagline. Give it that evidence, in the sources it trains on and the sources it searches, and you give it reasons to recommend you. Centium reads how each model talks about your brand and turns the patterns into a plan for what to feed it first.
see both exams, clearly.
You cannot fix what you cannot see, and AI answers are a black box by default. Centium opens it. We run hundreds of prompts across ChatGPT, Claude, Gemini, Perplexity, and Grok, at the category level rather than by naming your brand, so the data stays unbiased. We measure how often you surface, who surfaces instead, and then we ask the models what factors drove the decision and record every source they cited.
That gives you a read on both exams at once: whether the closed book already knows you, and where the open book looks when it searches. Your indexing footprint shows what AI has learned and whether it can reach your site, and your sources and citations show where it goes live. From there, Centium turns the data into a prioritized plan, so you are not just seeing how AI answers questions about your brand, you are doing something about it.
questions, answered.
Both, depending on the question. Every model carries a large base of knowledge it learned during training, and most can also search the web in real time when a question calls for fresh information. A single answer often blends the two: the model combines what it already knew with what it just found, then writes one response.
A knowledge cutoff is the date a model stopped taking in training data. Anything that happened after that date is not in the model unless it searches the web for it. Cutoffs are usually six to eighteen months behind the present, and a new version number does not always mean a fresher cutoff, since retraining on newer data is expensive and is not done on every release.
It can. When ChatGPT decides a question needs current information, it searches the web and cites what it finds, which you see as linked sources in the answer. But search is triggered selectively, not on every prompt, so a large share of answers still come straight from training data. That is why being present in both places matters.
Usually one of two reasons. Either your brand was thin or absent in the training data, so the model never learned much about you, or AI is not finding you when it searches the web live. New and smaller brands tend to hit the first problem, since established brands have years more content for models to learn from. The fix is to be present in both: the sources AI trains on and the sources it searches.
Yes. Each model favors different sources. ChatGPT leans heavily on Wikipedia, Gemini pulls from YouTube and Reddit, and Perplexity chases the most recent article it can find. The same question can produce different brands and different citations across models, which is why measuring one model is not enough.
Centium runs hundreds of prompts across five major models, measures how often each brand surfaces, asks the models what factors drove the decision, and records every source they cited. You get a clear read on where you stand in both the closed book and the open book, and what to do about it.
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