AI Visibility Audit

Why It Matters

Most brands have no measurement of their AI visibility, creating a blind spot in an increasingly important search channel. An AI visibility audit replaces assumption with data, identifying where the brand currently appears in AI answers, where it should appear but doesn't, and what specific changes will have the highest impact on citation rate.

How It Works

An AI visibility audit combines technical site analysis (crawler access, schema, page speed, content structure) with systematic AI platform querying (testing relevant prompts across multiple platforms), citation analysis (where the brand appears, with what sentiment and context), and competitive benchmarking (comparing citation rates against key competitors).

Use Cases

• Marketing teams establishing AI visibility baseline metrics before launching new content strategies
• SEO agencies conducting AI audits for clients entering new market categories
• B2B companies assessing AI visibility before major product launches
• Brands recovering from Google core update traffic drops using AI as an alternative channel
• Enterprise marketing teams presenting AI visibility data to C-suite stakeholders

Best Practices

• Test at least 50 relevant prompts across at least 3 major AI platforms
• Audit crawler access for all major AI bots (GPTBot, PerplexityBot, ClaudeBot, Google-Extended)
• Assess schema markup completeness across all major page types
• Score content AI-readiness against answer-first, entity density, and semantic depth criteria
• Compare citation rate against top 3 competitors for each prompt category
• Document audit findings in a prioritized action plan with effort/impact estimates