If you have ever typed your company name into ChatGPT, Google Gemini, or Perplexity and wondered "is this really how the world sees us?" — you already know the problem. AI search engines do not show ads, they do not offer a "search console" where you can submit your site, and they definitely do not tell you when or why your brand disappeared from their answers.
This is what people in the industry are starting to call Generative Engine Optimization (GEO) — a set of practices for understanding and improving how your brand shows up in AI-generated search results. Unlike traditional SEO, where Google's ranking factors are reasonably well understood, each AI model is a black box fed by a different mix of training data, real-time search results, and internal ranking signals.
What "visibility" means in the AI era
With Google, you had rankings: position 1 through position 100. You could measure it, graph it, and optimize for it. With AI search, the concept of "visibility" is fundamentally different in three ways:
Attribution is sparse. When ChatGPT lists five product recommendations, only one brand gets named. The other four might as well not exist. There is no "page 2" where you can still be found.
Context matters more than keywords. The same AI model may recommend your business when asked "find me a plumber in Lisbon" but ignore you completely when asked "emergency plumbing services Portugal" — even though the real-world intent is identical. Understanding which phrasing triggers a mention is the core of GEO.
Hallucination is a real risk. Some businesses discover that AI models have fabricated negative information about them — a competitor's review attributed to them, a service they never offered listed as a specialty, or worse, an incorrect address or phone number.
Why manual checking does not scale
You could open each AI tool and type queries manually. For one brand in one language on one model, that takes about 15 minutes. Now multiply by:
- 6+ AI search engines (ChatGPT, Gemini, Perplexity, Claude, Copilot, Grok)
- Multiple query variants per engine
- Multiple languages (pt-PT, pt-BR, EN, ES)
- Regular monitoring (weekly, not once)
The math lands at 20+ hours per week for a single brand. That is where a GEO tracking tool like GEO Tracker comes in — it automates the query execution across all major AI models and compiles the result into a report you can actually act on.
What the report should contain
If you decide to measure your AI visibility, here is the minimum set of data points worth collecting each week:
For each AI model:
- Did our brand appear? (yes/no/partial)
- In what context? (recommendation, mention, comparison, list)
- Positive, neutral, or negative framing?
- Was any incorrect information attributed to us?
Across all models:
- Which queries produce the highest mention rate?
- Which competitor appears most often across models?
- Which language has the best coverage?
What to do with the data
Once you have a baseline, the optimization loop is surprisingly similar to old-school SEO:
- Fill gaps — if a model never mentions you, check whether your structured data (Organization + LocalBusiness schema) is correct and crawlable
- Reinforce positive signals — when a model already recommends you in one language, publish equivalent content in your other target languages
- Correct hallucinations — if a model attributes wrong info to your brand, the fix usually requires updating authoritative sources (your website, Wikipedia, Crunchbase) and waiting for the next training cycle
- Track competitors — when a competitor appears where you don't, analyze what signals they have that you lack
Traditional SEO is not dead, but it is no longer the only game in town. Businesses that ignore their AI search presence today will find themselves invisible to an entire generation of users who never click "View all" — they just take the AI's answer and move on.
