Abstract: There is an acquisition channel that most businesses in Colombia are still not working on: being cited when an LLM answers a relevant question for your industry. This node documents what Generative Engine Optimization (GEO) is, how it differs from traditional SEO, and the five concrete adjustments that determine whether an LLM cites you or ignores you. Useful for business owners and marketing teams that already have published content and want that content to work in the AI ecosystem.
Three months ago, a client came to a diagnostic call saying they found my services "because ChatGPT recommended searching for automation providers with n8n in Colombia and your name came up." There was no advertising. There was no active campaign. There was a technical article with specific data, published six months earlier, that an LLM had indexed and was citing as a reference.
That was not luck. It was structure.
Why this is no longer optional: the numbers of change
Before explaining how GEO works, it is worth understanding why it matters now and not in two years.
ChatGPT crossed 1 billion weekly searches in 2025. Perplexity handles 780 million monthly queries. According to research by TTMS, 34% of users already use an LLM daily or almost daily, and more than half of consumers have tried LLM-based search.
The traffic that used to come from Google is migrating — and what is lost is not just volume. Organic traffic to websites fell from 2.3 billion monthly visits in mid-2024 to less than 1.7 billion in May 2025 — over 600 million visits lost in 12 months. 60% of all searches on Google now end without any clicks to any website.
But the flip side of those numbers is what makes GEO urgent for a B2B business: traffic referred by LLMs grew 527% year-on-year between January and May 2024 and the same period in 2025 (Search Engine Land). And that traffic converts 23 times better than traditional organic traffic (Ahrefs) — visitors coming from an LLM have already been filtered by intent; they arrive ready to act.
50% of B2B software buyers now start their research in AI chatbots (G2), and only 16% of companies currently track their visibility in AI search systematically. That gap — the majority of buyers using LLMs, the minority of providers optimizing for them — is exactly the window that exists today in Colombia and in Spanish-speaking markets.
What is GEO and how does it differ from the SEO you already know?
Generative Engine Optimization (GEO) is the set of practices that determine whether your content is cited when a language model — Claude, ChatGPT, Gemini, Perplexity — answers a user's question.
Traditional SEO optimized for position in search results: Google showed you among the top ten links. GEO optimizes for something different: for the LLM to extract information from your content and use it as a direct answer, citing you as the source.
The most important practical difference is not technical — it is commercial. When ChatGPT cites web pages, approximately 90% of the cited pages occupy positions 21 or lower in traditional organic search results. That means you do not need to be first on Google to be cited by an LLM — you need content that answers specific questions with verifiable data.
For a B2B service business in Colombia, where trust and technical specificity are the most important buying filters, that is not organic traffic — it is qualified recommendation from a source that the buyer already consults before contacting any provider.
Why do LLMs cite some content and not others?
Language models do not cite out of sympathy. They cite because the content has characteristics that make it extractable and verifiable. From my direct observation testing how different models respond to questions on topics where I have published content, there are five factors that determine whether content is cited or ignored:
Claims with data + context + declared source. "The costs of automation with n8n were reduced by 60% to 80% in three service projects implemented in Ibagué between 2024 and 2025" is citable. "Automation significantly reduces costs" is not. The model needs something it can extract as a unit of verifiable information. Without data, context, and source, the content has nothing to cite.
Headings that answer real questions. LLMs use the document structure to navigate the content. A heading like "How long does it take to implement a CRM with n8n for a small business?" directly answers the question someone would ask the model. A heading like "Implementation considerations" has no useful semantics for the model — it is a label, not an answer.
Geographical and contextual specificity. Generic content competes with millions of pages in English that models have indexed much more. Specific content — "meliponiculture in the Ambalá-Calambeo corridor, Tolima" or "process automation for small businesses in Ibagué" — has less competition and more relevance for local or regional searches. An LLM responding about providers in Colombia prefers to cite Colombian sources with Colombian data.
Complete technical terms the first time. "GEO (Generative Engine Optimization)" before abbreviating. "Tetragonisca angustula (angelita)" before using only the common name. Models connect terms with their definitions — if you introduce the full term with its expansion, the model can cite you when someone searches for either form.
Extractable structure in comparisons and lists. A table comparing tools with explicit criteria — name, cost, use case, limitation — is much more citable than the same content in narrative paragraph form. The model can extract the table as a unit and cite it in a response about "which tool to use for X."
How this looks applied: the same content, non-citable version and GEO version
Non-citable version:
"At Espacios Plus, we work with high-quality local wood to create carpentry solutions that optimize our clients' spaces with functional and aesthetic designs."
GEO version:
"At Espacios Plus, we use Nogal Cafetero (Cordia alliodora) sourced from sawmills in the Ambalá-Calambeo corridor for multifunctional furniture in spaces smaller than 40m². In the last 12 space optimization projects in Ibagué, the average increase in storage capacity was 35% without expanding the built area."
The second text answers specific questions someone would ask an LLM: what wood is used in carpentry for small spaces in Ibagué? how much does storage improve with multifunctional furniture? The first version does not answer any questions — it declares.
How long does it take to work?
I do not have enough data to give a number with certainty, and anyone who gives you a precise deadline is speculating. What I can say from my experience: the article that generated the inquiry mentioned at the beginning of this node took approximately four months to appear cited repeatedly in ChatGPT responses about automation with n8n in Colombia. Astro v5 with Strapi as CMS, well-configured XML sitemap, content indexed in Google before appearing in LLMs.
The LLM channel is slower to activate than SEO, but more persistent once active — the model continues to cite the content even without recent updates, as long as the content maintains specificity and structure.
What is still unclear
GEO is a field less than two years old as a documented practice. What I know comes from direct observation, not from controlled studies with large samples. Specifically, I do not have clarity on:
What weight different models give to the publication date versus content quality. In SEO, freshness matters a lot; in GEO, I have no clear evidence that it is the same.
If the citation behavior varies significantly between models. What works for ChatGPT to cite you may not be the same that activates Claude or Perplexity. I am actively observing this and will update this node when I have more data.
If there are relevant differences between content in Spanish and English beyond the lower competition in Spanish. Intuition says yes, but I do not have enough cases to affirm it with data.
Do you have published content that describes what your business does? The most useful question to start with is not "how do I optimize for GEO?" but "do my current texts contain claims with data, context, and source, or do they just declare?" That is the diagnosis.
Sources cited in this node:
- TTMS Research — daily adoption of LLMs, 2025
- Evolv Agency / DemandSage — ChatGPT 800M weekly users, 2025
- Search Engine Land / Previsible AI Traffic Report — growth of traffic referred by LLMs 527% year-on-year, 2025
- Ahrefs — conversion rate of traffic referred by AI 23x vs organic
- G2 — 50% of B2B buyers start research in AI chatbots
- Semrush / Insightland — 90% of pages cited by ChatGPT occupy position 21+ in organic search
- Bain & Company / Superlines — 60% of searches on Google end without a click, 2025
- SE Ranking, June 2026 — traffic referred by Claude grew 4x in 2026