GEO / AEO

What is GEO/AEO, and why now?

GEO (generative engine optimization) and AEO (answer engine optimization) are two names for the same shift: optimizing not to rank on a results page, but to be the source an AI cites when it answers. A buyer asks ChatGPT or Perplexity for the best option, the assistant synthesizes a reply, and a few brands get named. GEO/AEO is the discipline of being one of them — consistently, across engines.

Why now is simple: the behavior already moved. 73% of B2B buyers use AI in their research, AI overviews reach billions of people a month, and more than half of US searches now end without a single click. For a growing share of buyers, the AI's answer is the first page. A brand that's invisible there loses customers it never even sees — and most competitors haven't adjusted, which is exactly why the window is open.

How AI decides who to cite

Here's the part that reshapes the strategy: AI doesn't rank you the way Google does — it looks for agreement across independent sources. The data is blunt: 82–95% of AI citations come from earned, third-party media, while your own website is only about 5–10% of what the model consults. Brand mentions on trusted sites correlate roughly three times more with AI visibility than backlinks do. If credible sources don't describe you consistently, AI has nothing to verify you with, so it leaves you out.

Content built to be quoted

AI doesn't quote vague marketing copy; it quotes clear answers. So we structure your content to be liftable: direct question-and-answer sections, concrete figures with sources, plain definitions, and depth where it counts — thorough pages get cited more often than thin ones. The goal is that when a model scans your page, it finds a clean, quotable answer to the exact question a buyer asked, rather than a paragraph it has to interpret and probably skips.

This is where native-per-language matters. A page written from scratch for an English-speaking buyer uses different questions and phrasing than one written for a Spanish-speaking one, and AI rewards that authenticity. Translated copy reads as translated and competes weakly for the terms each audience actually uses. For brands serving more than one market, we build the citable content per language, not converted from another — because the engine is comparing you against sources written natively for that audience.

The technical side AI actually reads

There's an engineering layer beneath all of this, and it's where many sites quietly fail. AI crawlers run very little code: if your content only appears after scripts execute, to them it barely exists. So we ensure your pages render their content server-side, expose a clean structure, and carry schema markup that states plainly what you are — which can lift visibility and citations because it removes the guesswork for the machine. Publishing an llms.txt and allowing the right AI crawlers rounds out the basics.

// Be readable, understandable, and described by others
{
  "readable": "server-rendered HTML, llms.txt, crawlers allowed",
  "understood": "schema + a consistent brand entity",
  "citable": "Q&A structure, sourced figures, native per language",
  "trusted": "earned mentions + accurate listings",
  "kpis": ["AI citations", "share of voice", "answer presence by engine"]
}

Measuring something most marketers ignore

You can't improve what you don't measure, and AI visibility is the rare thing almost no one tracks: only about 22% of marketers monitor it, and 64% aren't sure how to measure AI success at all. That gap is an advantage for whoever closes it. We track where you're cited and where you're not, across engines, for the prompts your buyers actually use — turning "are we showing up in AI?" from a guess into a number you can move.

That measurement also keeps the work honest. AI-referred visitors convert about five times better than Google organic, so presence in those answers is worth real money — but only if you can attribute it. We connect AI citation tracking to share of voice and to the traffic and conversions it drives, so the investment proves itself the same way every other channel here does: with figures, not faith.

Different engines, different rules

One of the most practical things to plan for is that the engines don't agree. ChatGPT leans heavily on business listings, Gemini favors websites, and Perplexity pulls from review platforms and community sources like Reddit. A brand prominent in one can be completely absent from another, and the mix keeps shifting as the platforms rebalance. Optimizing for "AI" as a single thing is a mistake; each engine has its own sources of truth.

So we don't chase one assistant — we build a consistent, citable presence across the sources each engine trusts, then watch which ones name you and which don't. That breadth is also a hedge: as referral share moves between ChatGPT, Gemini, Perplexity and others, a brand described coherently everywhere keeps showing up no matter which engine your buyer happens to open.

What changes for local, B2B and international

The discipline is the same; the emphasis shifts with who you sell to. A local business wants to be the answer when someone asks an AI for "the best option near me," so the work leans on consistent listings, reviews and location data that ChatGPT in particular draws on. A B2B company wants to be on the shortlist an assistant builds during vendor research, which depends more on in-depth, citable content and earned mentions in industry sources, since B2B buyers use AI to compare and qualify before they ever contact sales.

For international clients reaching Latin America, GEO is an unusually open field: AI search is global, regional buyers are adopting it fast, and most local competitors haven't started optimizing for it. The requirement is to be described natively per market and language, because the engine compares you against sources written for that audience. Done early, this wins a position in the answers that's hard for a slower competitor to dislodge later.

The mistakes that keep brands uncited

Most invisibility in AI search comes from a handful of avoidable errors. The first is treating GEO as on-page SEO with new keywords: stuffing terms does nothing if no credible third party corroborates you, since AI weighs outside agreement far more than self-description. The second is inconsistency — a different name, claim or set of facts across your site, listings and profiles — which gives the model conflicting signals and a reason to leave you out rather than risk citing something unreliable.

The third is content a machine can't cleanly lift: long, vague paragraphs with no direct answer, or pages whose text only loads after scripts run. And the fourth is simply not measuring, so you never learn which engines name you or for which questions. We start by auditing exactly these points — corroboration, consistency, citability and tracking — because fixing them is usually what moves a brand from absent to cited, without waiting for some future tactic.

How we work

We build your AI-search visibility as a system, on-site and off:

• Foundation: server-rendered content, llms.txt, crawler access and schema so AI can read and understand you.
• Citable content: question-led, sourced, native per language and structured to be quoted.
• Entity consistency: the same brand facts across your site, listings and the web.
• Earned authority: mentions and coverage that give AI credible third parties to cite.
• Measurement: citation tracking and share of voice by engine, tied to traffic and conversions.

The result is presence in the answers your buyers now trust first. It pairs naturally with local SEO and digital PR, and it's the difference between hoping AI knows you and engineering it to — while most of your competitors are still treating search like it's 2019.

Frequently asked questions