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.
How the engines actually work - and what that means for your visibility.
How to find GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot and PerplexityBot in your logs, verify their IPs, and read training versus live-fetch patterns.
How Microsoft Copilot grounds answers in Bing, how it differs from ChatGPT search, and how Bing Webmaster Tools, IndexNow and AI Performance get you cited.
A mention is your name in an AI answer; a citation is your page linked as a source. How engines show each, share of voice vs citation share, what moves both.
What ChatGPT, Gemini and Perplexity recommended - and cited - across law firms, clinics, ecommerce, hotels and six more industries in the US, UK, Canada, Spain and Turkey. 79% of cited domains appeared exactly once, ChatGPT and Gemini named the same #1 brand in 8.5% of questions, and Wikipedia made up 9.5% of ChatGPT's citations but none of Perplexity's.
App Store Optimization was built for one reader: the store ranking algorithm. Now your listing has a second reader - the AI assistants people ask for app recommendations - and it grades on different criteria. What ASO is, what GEO changes about it, where the two disciplines pull in opposite directions, and the listing playbook that satisfies both readers at once.
People keep googling strings like turn0product7 and turn0search0 - and finding storefront junk instead of an answer. Here's the real one: they're ChatGPT's internal citation tokens, the invisible markers that place product cards and source badges inside an answer. Where they come from (we've captured them in the raw response stream), why they leak into copied text, and what their existence tells brands about how AI shopping answers are assembled.
Video keeps showing up as a source in AI answers. Why engines cite YouTube so often, what they actually read from it, and how to make your video content quotable to a model that can't watch it.
Payment giants are building rails for AI agents that discover, decide and check out for people. What agentic commerce means for brands - and why being the answer the agent trusts is the new shelf position.
An honest look at our own scan data: across 2,209 answers, only 39% cited a source - and which engine you ask changes everything. Plus the recognisable domains that keep showing up, and how fragmented the rest is.
Raw mentions is a vanity metric. The numbers that tell you whether AI engines recommend your brand - visibility rate, share of voice, answer position, citation share, sentiment and engine coverage - and how to read each one.
The numbers behind the shift from Google to AI answers - engine reach, click-through, how buyers research, and how ChatGPT picks and cites the brands it names, including the August 2026 changes. Every figure links to its source, and vendor research is labeled.
ChatGPT, Gemini, AI Overviews, Perplexity, Claude, Copilot, Grok and the rest - who's behind each, whether it answers from the live web or from memory, and how every one of them decides which brands to name.
Short answer: not the recommendation. ChatGPT now tests labeled ads and Google sells ads in AI Mode, but neither buys you a place in the answer. What's actually for sale in October 2026 - and what the brands that get recommended did instead.
Yes - but its job changed. Why your Google rank no longer guarantees an AI mention, what SEO still feeds into every engine, and where to put this year's effort.
The answer that names you today can drop you next week. The three things that make AI answers drift - and a sane monitoring cadence that catches it without obsessing.
Where llms.txt stands in October 2026: Google says its search ignores the file, and Ahrefs found 97% of ~38,000 llms.txt files got no requests in a month. What the file does, what it can't do, and the 20-minute version if you ship one anyway.
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.
Each engine builds its answer differently - training data, retrieval, citations. The mechanics explain why your visibility differs per engine, and what to do about each one.
Community threads punch far above their weight in both training data and retrieval. Why that happened, what the August 2026 ChatGPT citation data does (and doesn't) show, and how to participate without astroturfing.
Publishers block them; brands mostly shouldn't. What each crawler does, what blocking actually prevents, and a sane robots.txt policy for a SaaS.
The most transparent engine is the best one to learn from: how retrieval, ranking and citation work in Perplexity, and what reliably gets a page into the answer.
You're on page one of Google, customers love you - and ChatGPT draws a blank. The three reasons this happens, and which one is yours.
Position, frequency, sentiment, sources: the components that make a visibility score honest, and the shortcuts that make one meaningless.
Research posts explain how the engines work and what the data shows: where answers come from, which sources get cited, how often answers change and why engines disagree about the same question.
One practical essay on AI visibility every other week. No fluff, unsubscribe anytime.