How To Run An AI Visibility Audit With An Agent
A review-first guide to the documented BotSee /ai-visibility-audit workflow: set up a site, refine buyer questions, inspect evidence, and propose text-only changes for approval.
Twelve practical guides for measuring, improving, and reporting how brands appear in AI answers.
If you lead growth, SEO, or product marketing and need a clear AI visibility system, start here. We focus on signal quality, reproducible tests, and compounding distribution loops.
Every post includes a short scan-first summary at the top, followed by long-form implementation depth underneath so teams can move quickly without losing the full SEO and AEO context.
Use the library in the order your team needs: define the baseline, investigate gaps, then run a repeatable reporting loop.
A review-first guide to the documented BotSee /ai-visibility-audit workflow: set up a site, refine buyer questions, inspect evidence, and propose text-only changes for approval.
A review-first BotSee workflow for defining a site and buyer questions, running analysis, retrieving evidence, and proposing content work without autonomous publishing.
A technical operator's guide to running BotSee from Claude Code, Codex, OpenClaw, or Hermes: choose the supported install path, run workflow commands deliberately, and handle stdout, JSON, and errors correctly.
Learn what changes when teams move from rankings-only SEO reports to AI visibility reporting across ChatGPT, Claude, Gemini, and Perplexity.
Learn what AI visibility monitoring measures, how to build a useful prompt library, and how to turn structured answer-engine results into content and positioning work.
A practical workflow for measuring how AI answers change across markets, languages, and buyer contexts before you make the wrong expansion decisions.