How ChatGPT, Claude, Perplexity, and Google AI describe and recommend brands — how to measure it, why it goes wrong, and what actually changes it. Each cluster below opens with its pillar article.
Metrics, prompts, providers, and repeated runs — what to count, and what makes two numbers comparable.
One AI visibility scan shows what happened once. Repeated scans show whether your brand is reliably visible across prompts, competitors, citations, and time.
6 min read
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.
12 min read
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.
14 min read
Mention share tells you whether AI names your brand. Citation share tells you which sources AI answers actually cite in your category — and for fixing visibility, it is usually the more actionable metric.
13 min read
SEO signals can matter, but AI visibility associations differ by category, buyer question, provider, and market. Learn how to measure category-specific evidence without mistaking correlation for causation.
12 min read
One scan is not a benchmark. Compare your brand with competitors under identical conditions, then track score gaps and trends across repeated measurement windows.
11 min read