Guides and methodology on measuring AI visibility — how ChatGPT, Claude, Perplexity, and Google AI describe and recommend brands, and what it takes to be named first.
One AI visibility scan shows what happened once. Repeated scans show whether your brand is reliably visible across prompts, competitors, citations, and time.
AI assistants answer questions, they don’t rank pages. Three pillars make your site the material answers are built from: machine-legible markup, question-shaped content, and verifiable credibility.
AI recommends the businesses it can understand, verify, and source most confidently. A five-part diagnostic shows exactly where competitors beat you — and what to fix first.
A repeatable audit workflow: build a real buyer prompt set, run it across AI providers, and track mentions, recommendations, citations, share of voice, and sentiment — then turn the cited-source gaps into a fix backlog.
AI answers compress local trust signals into a three-to-five business shortlist. This guide covers the layer that gets you on it — Google Business Profile, reviews, NAP consistency, service pages, schema, and third-party mentions.
Reddit shows up in AI citations because it holds real buyer language and lived experience. The durable strategy is authentic participation plus answer assets on your own site — and measuring whether community sources actually appear in AI answers.
Mention share tells you whether AI names your brand. Citation share tells you which sources AI trusts enough to ground the answer — and for fixing visibility, it is usually the more actionable metric.
There is no single “AI search” result. Engines differ in retrieval, indexes, browsing behavior, citations, and freshness — so the same prompt returns different brands. Here is why, and how to measure and fix visibility per provider.
Good AI visibility work starts with measurement and source diagnosis. Bad work sells guaranteed ChatGPT rankings, fake mentions, and mass AI content. The questions, red flags, and deliverables that separate the two.