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
The competitive cohort, the SEO/AI divergence, factual accuracy, and the evidence a page never states to machines.
When an assistant names a competitor instead of you, five evidence gaps are worth checking first — entity clarity, reviews, citations, third-party mentions, and citable pages. A diagnostic hypothesis set, not a claim about how the model decides.
12 min read
A polished local business website can still be hard for AI to understand. This teardown shows why missing schema, crawlable facts, review proof, and agent-ready contact paths cost recommendations.
11 min read
Page-one SEO rankings do not always turn into AI recommendations. This guide shows the topic-level gaps, source proof, and fix queue behind that visibility split.
8 min read
The businesses appearing beside you in Google may differ from the brands AI recommends. Learn how to measure the overlap and track influence sources separately.
11 min read
AI can name your business yet get its services, location, hours, or identity wrong. Use a documented evidence-repair workflow to investigate errors and re-measure accuracy.
7 min read
Owned pages, primary profiles, independent sources, and reviews — scoped into work you can fund and verify.
Replace vague AI visibility advice with competitive targets your team can scope, fund, and verify across reviews, listings, content, citations, structured data, and technical readiness.
10 min read
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.
10 min read
Turn observed AI citations into an evidence-aware placement plan: what to build, where to earn trustworthy presence, and how to measure the result without overclaiming.
13 min read
A practical, evidence-aware guide to llms.txt: purpose, current limits, safe format, how it differs from robots.txt, sitemaps, and schema, and when it is worth maintaining.
6 min read
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.
13 min read
Google Business Profile, reviews, NAP consistency, service pages, and the local entity evidence behind a shortlist.
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.
14 min read
An anonymized local-service teardown of why AI assistants recommend some businesses and skip others: entity clarity, third-party proof, citable pages, structured data, and re-scan measurement.
9 min read
Measured engagements and recurring source observations, with their methods stated.
An anonymized AI visibility case study for a local window film company: 4.62% AI visibility, 12 mentions across 260 AI answers, Google AI Overview at 0%, and the roadmap to improve local AI search visibility.
15 min read
Reddit may influence AI visibility today and fade tomorrow. Learn why sources can change by model, product, market, and month—and how to re-measure them.
13 min read
What to ask a vendor, which artifacts to demand, and the red flags that should end a pitch.
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.
13 min read
An unranked buyer guide to AI visibility tracking tools, with public prices, prompt limits, stated coverage, and plan caveats.
4 min read