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.
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