GEO agency Mexico
Hero
Two acronyms search this page. Defined once, then straight to what matters.
- GEO (Generative Engine Optimization): the practice of making a brand more likely to appear in the answers of generative AI engines.
- AEO (Answer Engine Optimization): the same goal aimed at answer engines, the search results that respond directly instead of listing links.
What matters is whether AI recommends you. We measure that.
Kept in Spanish for the final site: “Lo que importa es si la IA te recomienda. Eso lo medimos.”
Whether you searched for an agency, a consultant or the acronyms, the question underneath is the same: when someone asks an AI engine about our category, who gets named?
The debate, settled in one line
Some agencies sell GEO as a secret new framework. Others say it is hype and “just SEO”. Both are half right, and the halves do not cancel out.
Google’s own guide settles the optimization half: “optimizing for generative AI search is still SEO” (Search Central, “Optimizing for generative AI search”, updated July 2026: https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). The same guide names llms.txt, chunking and special schema as myths. So the skeptics are right: the optimization is mostly solid SEO, and anyone selling magic files is selling what Google says does not exist.
The skeptics stop one step short. Ask them how they know whether ChatGPT recommends you in your category this month, against which competitors, citing which sources. They cannot say. Measurement is the missing half, and it is not SEO. It is a study with a frozen prompt bank, repetitions and a codebook.
Method and proof
The method, in three steps: we build your prompt bank from your category and documented demand, we measure across four AI engines with repetitions, we read every response and count the same things every time. Full method: /metodologia/.
What a report reads like, from the invented sample (a sunscreen brand sold in Mexican pharmacies, measured against its category competitors Bloqsol and Dermalux):
- Mentioned (mencionada): 26%, 103 of 396 responses
- Recommended (recomendada): 11%, 44 of 396 responses
- Named as the best (señalada como la mejor): 3%, 12 of 396 responses
- With a purchase path (con ruta de compra): 7%, 28 of 396 responses
Every rate prints its denominator. A percentage with no population behind it is a decoration.
Marca inventada, competidores inventados, cifras inventadas. La estructura es real; los números no son de nadie.
Sample one-pagers: the consumer example above and a B2B example, same structure, same disclaimer.
The demo: we measure our own category
This page is the home of a self-referential study: who AI engines recommend for “agencia GEO México”, measured with the same frozen bank, the same repetitions and the same codebook as any client study, published with raw output included.
If the numbers look bad, they get published anyway. An evidence-first agency shows its own baseline. [VERIFICAR: pending first run]
Until the first run is published, the method is open for inspection: /metodologia/.
Built for Mexico
Almost everyone in this market quotes US studies. We generate Mexican data.
- Prompts in Spanish, written the way Mexican buyers actually phrase them, not translated US prompt lists.
- Local competitor sets, including the private-label trap: models routinely treat a premium brand as interchangeable with the supermarket brand next to it on the shelf. If your list only carries the brands your sales team respects, your share number is wrong in a way nobody notices.
- Country-level answer behavior. Assistants answer differently by market. A study run on US data does not describe what a buyer in Mexico City sees.
Spanish prompts, local competitors, local sources. That is what “agencia GEO México” should mean, and what the published demo will measure.
Start with the question, not the tool
The right first step is not a framework and not a package. It is the question this page exists for: who do AI engines recommend in your category, and is it you?
Request the study. It starts with a conversation about that decision, not with a demo.