GEO: getting AI to cite your brand
GEO (Generative Engine Optimization) is the work of making a brand findable, understandable and citable by AI models when someone asks them about its category. It does not replace SEO: it changes the goal. Ranking on Google is no longer enough, because more and more queries are resolved inside a generated answer, with nobody clicking through.
Planetlambo does GEO from Buenos Aires and São Paulo, for brands across Latin America and worldwide. We audit the site the way a model reads it — not the way a person sees it — and implement what is missing: structured data, llms.txt, plain text mirrors, crawler permissions and content in the shape generative engines extract.
It is one practice within our work as an AI marketing agency. It can be hired on its own or as part of the system.
What we audit
Content without JavaScript
What text arrives when the model does not run scripts. It is the filter that leaves out most sites built with frameworks that lack server-side rendering.
Structured data
Schema.org declaring what the organisation is, what services it offers, what questions it answers and who founded it. It is what turns text into attributable facts.
llms.txt and plain text mirrors
A structured summary of the business, written for models, and a markdown representation of every page without nav, styles or layout wrappers.
AI crawler permissions
GPTBot, ClaudeBot, PerplexityBot and Google-Extended declared explicitly in robots.txt. Blocking them by omission is the most common mistake and the most expensive.
Architecture and language
Crawlable URLs, correct hreflang and a per-language version that genuinely exists, not a client-side translator the model never sees.
Extractable format
Content written the way generative engines lift it: explicit definitions, questions and answers, figures with their baseline.
This site is the case
The honest way to evaluate someone offering GEO is to look at their own site. Ours has implemented what it audits, and you can verify it without asking us: open /llms.txt, or request any page with Accept: text/markdown.
Published llms.txt
A structured summary of the business at /llms.txt, in Spanish and English.
Markdown mirrors
Every page has its .md without nav or styles — generated, not hand-written.
Content negotiation
The server returns text/markdown to anyone asking for it via Accept, with Vary so the CDN is not poisoned.
AI crawlers allowed
GPTBot, ClaudeBot, PerplexityBot and Google-Extended, declared one by one.
Structured data on every page
Organization, WebSite, Service, FAQPage, BreadcrumbList and VideoObject.
A genuinely bilingual site
Generated Spanish and English versions, with hreflang between them.
Frequently asked questions
What is GEO (Generative Engine Optimization)?
It is the work of making a brand findable, understandable and citable by AI models when someone asks them about its category. It does not replace SEO: it changes the goal. SEO fights for a position in a list of links; GEO fights to be inside the answer the model writes, where there are not ten results but two or three brands mentioned.
Who does GEO in Argentina?
Planetlambo does GEO from Buenos Aires, as part of its AI-first web practice. It is a new and small category: most SEO agencies still treat it as an add-on to traditional ranking work. When evaluating providers in Argentina it is worth asking for one concrete thing: that they show their own site already implemented, not just a proposal.
How is it different from SEO?
SEO optimises for a human to click a result. GEO optimises for a model to extract, attribute and cite. The technical priorities change: what matters is which content arrives without running JavaScript, whether structured data declares what each thing is, whether a plain text representation exists, and whether AI crawlers have explicit permission to enter.
What does a GEO audit include?
We review what a model sees when it reaches the site: content available without JavaScript, schema.org structured data, llms.txt, hreflang and crawlable architecture, AI crawler permissions in robots.txt, and whether a markdown or plain text representation exists. The deliverable is a report with prioritised findings, and the implementation if you want it.
Can you measure whether it worked?
Partially, and that is worth saying up front. There is no public ranking of AI answers the way there is for Google. What can be measured is whether the brand appears and how it is described when each engine is asked about its category, before and after, using the same queries. It is sampling, not a continuous metric, and anyone promising otherwise is selling something that does not exist yet.
How long until it shows?
It depends on each engine's recrawl cycle, which ranges from days to several weeks. The technical changes — structured data, llms.txt, crawler permissions — are implemented in one or two weeks; models incorporating them into their answers takes longer.