AIEO, GEO and SEO — how they relate

Three terms dominate this field, and they are often used loosely. Here is the vocabulary this site uses — keeping it precise matters, because each term implies a different kind of work.

The three termsCopy Anchor Link

SEO (Search Engine Optimization) is the established practice of making websites findable in search results: crawlable pages, clear structure, relevant content, and authority earned over time. Decades of documented practice.

GEO (Generative Engine Optimization) refers to adapting content for systems that generate answers rather than list links. The term became popular with academic work on how generative engines select and present sources.

AIEO (AI Engine Optimization) is used largely as a synonym for GEO — with more emphasis on AI assistants (chat products, agents) rather than answer-style search engines. This site uses AIEO for the product and treats GEO as the shared research term.

In practice the three overlap heavily, because generative systems still depend on the open web: they fetch, index, and read pages built for humans.

What actually changes with AI answersCopy Anchor Link

An AI answer system typically combines three layers. Knowing which layer you are influencing prevents most confusion:

1. A model — trained on large corpora, mostly frozen at training time. You generally cannot optimize for the frozen model retroactively.
2. A retrieval layer — search or browsing that fetches current pages when a question is asked. This is where your site's accessibility and content quality matter today.
3. An answer composition step — the model synthesizes an answer from retrieved sources, sometimes with citations, sometimes without.

The practical consequence: most of what you can influence lives in layers 2 and 3 — can your pages be fetched, are they readable without a browser, do sections stand on their own, are claims supported.

What does not changeCopy Anchor Link

If your site is in reasonable SEO shape, you have already done most of what AIEO asks for:

• Crawlability — pages reachable, sensible robots policy, server-rendered content. See AI crawlers and access control.
• Clear metadata — titles and descriptions that describe the page. See the first site check.
• Evidence-backed content — claims with sources, data, and examples.
• Self-contained sections — headings that correspond to real questions, sections that make sense when quoted alone.

What is genuinely new is measurement: observing how assistants describe your domain requires new instruments — and new honesty about what small samples can tell you.

What honest optimization looks likeCopy Anchor Link

The loop this site and product are built on:

1. Understand the conditions (crawlability, structure, metadata).
2. Look at evidence — specific findings, not scores.
3. Improve what applies to you, with reasoning you can write down.
4. Verify by comparing later observations under similar conditions.

And what it is not:

• No one can guarantee that an AI system will recommend or cite you. Anyone promising that is selling certainty that does not exist.
• Hiding text, stuffing keywords, or injecting "instructions for AI" into pages is manipulation, not optimization. It violates search guidelines and adds nothing for answer quality.
• One anecdote — "I asked ChatGPT and it didn't mention us" — is not a measurement. Samples, denominators, and dates are.

Reading the rest of the guidesCopy Anchor Link

• Your first site check — a concrete procedure for the technical basics.
• AI crawlers and access control — who fetches your pages and how to reason about access decisions.
• Rule reference — the vocabulary used by findings in reports and demos.