AI crawlers in your server logs: GPTBot, ClaudeBot & co.
How to find GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot and PerplexityBot in your logs, verify their IPs, and read training versus live-fetch patterns.
The acronym soup, sorted: what GEO (generative engine optimization), AEO (answer engine optimization) and their synonyms each cover, where they overlap, and why the distinctions matter less than the two real disciplines underneath them.
GEO, AEO, LLMO, AI SEO, AISO - the discipline of being visible in AI answers is young enough that nobody agrees what to call it, and the acronym soup makes a fairly simple landscape look complicated. Underneath the five names, only two things are actually being optimized.
Strip the branding away and there are only two disciplines underneath:
When an engine searches the live web to build an answer, do your pages get found, and are they quotable once opened? This is closest to classic SEO - crawlability, clear claims, structure, markup - and it moves on a fast clock: fix a page, and a retrieval-heavy engine like Perplexity can pick it up within days.
When a model answers from training data, does it know you exist? This is about presence in the corpus - reviews, comparisons, community threads, documentation. It moves on a slow clock measured in months, because nothing you publish today reaches the model until its next training run.
More than ever, but as a means as well as an end. The pages engines retrieve are disproportionately pages that already rank - AI search and classic search share infrastructure (ChatGPT's browsing leans on Bing's index, Gemini on Google's). Good SEO is now also GEO groundwork. What changed is the scoreboard: ranking #3 means little if the AI answer above the results names two competitors and not you.
We say GEO because it's what our own users type - and because, building this tool in public, "generative engine" is the one part of the acronym soup that names the thing we watch all day. But don't let vocabulary consume strategy: pick the questions you need to win, measure where you stand engine by engine, and work both clocks. The full guide walks through the framework.
The name will settle on its own; your spot on the shortlist won't. See where you stand while everyone argues about the acronym.
AEO is optimizing to be the answer rather than a result - originally for featured snippets and voice assistants, and today for AI systems like ChatGPT, Gemini, Perplexity and Google's AI Overviews. In current practice it's used interchangeably with GEO: the work underneath - question-shaped quotable content, structured data, corroboration across sources, measuring the answers per engine - is the same regardless of which acronym the vendor prefers.
SEO is the established discipline of ranking pages in classic search results. AEO - Answer Engine Optimization - is about being the answer rather than a result, across featured snippets, voice and now AI responses. GEO, Generative Engine Optimization, narrows that to brand presence in generative AI answers specifically, like ChatGPT, Claude, Gemini and Perplexity.
Effectively, yes - LLMO, AI SEO and AISO are synonyms for GEO coined in different corners of the industry, with no meaningful technical difference between them. The acronym soup makes a simple landscape look complicated. Strip the branding away and you're only ever optimising two things: being retrievable when an engine searches live, and being remembered from training data.
More than ever, but now as a means as well as an end. The pages engines retrieve are disproportionately pages that already rank, because AI search and classic search share infrastructure - ChatGPT's browsing leans on Bing's index and Gemini on Google's. What changed is the scoreboard: ranking third means little if the AI answer above the results names two competitors and not you.
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