AEO Is the New SEO: How to Get Your Brand Cited by ChatGPT, Claude & Gemini

A growing share of your buyers no longer start at Google. They ask ChatGPT, Claude, Gemini or Perplexity — "who are the best enterprise AI development companies?", "how do I deploy AI on legacy systems?" — and they act on the answer. If your brand isn't in that answer, you're invisible to demand you never even see.

That shift has a name: Answer Engine Optimization (AEO), sometimes called Generative Engine Optimization (GEO). It's not a replacement for SEO so much as its next layer. The goal moves from "rank on page one" to "get cited in the answer."

How answer engines actually choose sources

AI answers are assembled from two things: what the model already learned in training, and what it retrieves live from the web at question time. To be cited, your content has to be (a) findable by their crawlers, (b) easy to parse, and (c) clearly the most trustworthy, specific answer to the question. In practice that rewards a different style than classic keyword SEO:

• Direct, factual answers stated plainly, near the top — not buried under 600 words of preamble.
• Clear structure — real questions as headings, short paragraphs, lists, and a tone that's quotable.
• Evidence and specificity — numbers, named methods, sources. Vague marketing copy doesn't get cited; concrete claims do.

The technical foundations of AEO

Three technical pieces do most of the heavy lifting:

1. Let the AI crawlers in

Answer engines use their own crawlers — GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended and others. Your robots.txt should explicitly allow the ones you want citing you. Blocking them (often by accident) is the most common reason a brand never appears.

2. Structured data they can trust

Schema.org markup — Organization, FAQPage, Article, Person — turns your pages into machine-readable facts. A FAQPage that answers the exact questions buyers ask is one of the highest-leverage things you can publish, because it maps directly onto how people query AI.

3. An llms.txt summary

An emerging convention, llms.txt, is a plain-language file at the root of your site that tells AI models, in their own preferred format, who you are, what you do, who you serve and how to contact you. Think of it as a press kit written for machines. It's young, but cheap to add and increasingly read.

This very site practises what it preaches: crawler-friendly robots.txt, Organization + FAQ + Article structured data, and an llms.txt summary. AEO isn't theory for us — it's how kapis.ai is built.

Content is still the engine

Technical setup gets you eligible; content gets you cited. Publish genuinely useful, specific answers to the questions your buyers ask an AI. Demonstrate first-hand expertise (what Google calls E-E-A-T), put a real author behind it, and keep it current. One precise, authoritative article on a question you actually own will out-cite ten thin, generic posts.

How to measure it

AEO is harder to measure than blue links, but not impossible. Periodically ask the major models the questions your buyers would ask, and check whether you appear and how you're described. Track referral traffic from AI assistants in your analytics. Watch for the qualitative signal too — prospects who arrive already saying "I asked ChatGPT and it mentioned you." That's the new word-of-mouth.

The bottom line

Search isn't dying; it's being intermediated by models that summarise and cite. The brands that win the next few years will be the ones that are easy for those models to find, parse and trust. Allow the crawlers, mark up your facts, publish citable expertise, and add an llms.txt. It's early — which is exactly why it's worth doing now.