1. Why “Just Start Optimizing” Is Wrong
Skipping the diagnosis means treating the wrong illness
When a brand owner first realizes “we need to do GEO,” the most common next move is to bring someone in to optimize the site structure, add schema, and write more AI-friendly content.
This instinct isn’t wrong, but it skips a crucial preliminary step: if you don’t know how AI currently describes you, you don’t know what you’re fixing.
It’s like going to the doctor and starting to prescribe medication before taking your blood pressure, listening to your chest, or asking about your medical history. If the diagnosis is wrong, no amount of treatment will cure the illness.
Why GEO’s diagnostic blind spot is more serious than SEO’s
In SEO, the current state is relatively visible—you can look at GSC ranking data and traffic figures, so at least you know where to start.
How AI perceives your brand is something you almost never see in daily life, unless you actively ask it. And you need to do so across multiple AI engines, with multiple question framings, and on an ongoing basis—this observation work itself requires a system.
2. How AI “Learns” to Describe a Brand
Two sources of information
What an AI language model “knows” about a brand comes from two paths, and these two paths determine what AI says, how much it says, and whether it’s correct:
Path one: static memory from the training corpus
During training, the model reads vast amounts of text, including your official website, media coverage, social media, review platforms, and forum discussions. This information is retained in the model’s weights in compressed form, forming a baseline understanding. This understanding has a cutoff date and does not update in real time.
Path two: real-time retrieval augmentation (RAG)
Some AI tools (Perplexity, ChatGPT’s Search mode, Copilot) search the live web before generating an answer. This path lets AI’s description be relatively fresh, but it also means the answer depends heavily on “what it happened to find today.”
ChatGPT's underlying training corpus and Perplexity's real-time index are different, and each engine's RAG mechanism is different too, so the same question can yield very different answers across AI tools.
That's exactly why a brand perception diagnostic must be done across multiple engines—you can't rely on the result from just one.
3. The Three Layers of How AI Describes Your Brand
When you diagnose how AI describes your brand, the problem usually falls into three distinct layers, and each layer has a different root cause and a different direction for handling it.
Layer 1: Nonexistent (most serious)
You ask AI “Which companies in Taiwan do XX?” and your brand name doesn’t appear at all. Or you ask “What kind of company is [your brand name]?” and AI replies “I don’t have enough information,” or confuses you with some other entity that shares the same name.
Root cause: There is too little trustworthy content about you in the training corpus, almost no external third-party sources, and AI lacks enough material to form a clear description of your brand as an entity.
Difficulty of repair: Highest—building visibility in AI corpora takes time to accumulate; it’s not something where you change your website today and see results tomorrow.
Layer 2: Wrong description (high severity)
AI knows your name, but the description of your business is wrong: you mainly do B2B, and AI says you do B2C; your core strength is customized service, but AI says your distinguishing feature is “low prices”; you’ve already pivoted, but AI still describes you using your positioning from five years ago.
Root cause: Old training data dominates AI’s baseline understanding, or the content discussing you online leans more heavily in a wrong direction. Brands that have pivoted without synchronously updating their external information sources are especially prone to this problem.
A wrong description is sometimes harder to handle than "nonexistent"—you need to make the new, correct information override the old impression in AI's understanding. This requires systematically establishing the new, correct description across multiple channels, and ensuring this content has enough credibility.
Difficulty of repair: High—you need to systematically update correct information across multiple trustworthy external sources and wait for the AI model to relearn.
Layer 3: Weak competitiveness (medium severity)
AI does know you, and the description is roughly correct, but every time it’s asked “which companies do you recommend,” your brand ranks behind competitors, or only gets an occasional incidental mention after all the competitors have been listed.
Root cause: This isn’t a visibility problem; it’s that the completeness and clarity of your comparable attributes lag behind your competitors’. Your competitors have more specific feature descriptions, clearer use-case scenarios, and more third-party review support, so when AI generates a comparison list it more readily places them ahead.
Difficulty of repair: Medium—this is the layer that can most be acted on directly, and it’s also the most typical body of work in GEO optimization engineering.
4. The Limits of Doing the Diagnosis Yourself
Why asking AI once in a while isn’t enough
In theory you could open a few AI tools yourself, ask a few questions about your brand, and observe the results. But this kind of diagnosis has several structural problems:
Incomplete coverage. ChatGPT, Perplexity, Claude, Gemini, and Copilot describe the same brand very differently. Looking at only one AI’s result means missing a lot of information.
The question framing is too narrow. When you ask about your own brand, you tend to only ask “what is my company,” but consumers ask within a much wider range of framings: “what tool do you recommend for XX type of problem,” “which is better, A or B,” “whose solution fits best in this situation.” AI’s answers differ greatly across different framings.
A single snapshot is meaningless. AI’s citation behavior changes over time. One test can’t reveal a trend; what you need is regular tracking, so you can tell whether your optimization actions are working.
You ask ChatGPT "what is my company called," AI gives a roughly correct description, and you assume there's no problem.
But you didn't test: under the framing "recommended XX software for Taiwanese SMEs," does your brand appear? What about in Perplexity? How many times were competitors mentioned while you were mentioned only once? The answers to these questions are often completely different from the result of "just asking your own company name."
5. Only After Hearing Clearly Do You Know What to Change
The diagnosis determines the strategic direction
Even when the symptom is the same—“AI’s recommendation ranking isn’t good enough”—the root cause determines a completely different path for handling it:
| Diagnosis | Corresponding strategy |
|---|---|
| Nonexistent | Build a foundational corpus: external media coverage, trustworthy third-party sources |
| Wrong description | Update the correct positioning across multiple external channels, correcting where old information dominates |
| Weak competitiveness | Strengthen comparable attributes: specific features, scenarios, numbers, case studies |
Skipping the diagnosis and starting to optimize straight away means guessing which problem it is—and the odds of guessing wrong are higher than guessing right.
The free GEO health check is a systematic starting point; its 12-dimension scoring can help you identify your weakest link. If you need deep, cross-AI-engine brand perception analysis, get in touch: [email protected]
GEO brand strategy series. Previous article: Which brands disappear first from AI recommendation lists?