What is an AI search visibility audit?

TL;DR: an AI search visibility audit measures what AI engines — ChatGPT, Claude, Gemini, Perplexity — tell buyers about a brand. It runs real buying-intent prompts repeatedly, scores every answer (mentioned, cited, recommended, or invisible), benchmarks competitors, verifies what the engines claim, and ends in a prioritized fix roadmap. It is a diagnosis with evidence — not a score, and not a dashboard.

What does it actually measure?

• Mention rate — how often the brand appears in answers at all.
• Recommendation rate — how often engines endorse it as a pick.
• Citation rate — how often the brand's own site is used as a source.
• Share of voice — the brand's slice of all brand mentions in its category.
• Accuracy — whether what engines claim (prices, products, capabilities) is true.

What separates a real audit from a scan?

Repetition and breadth. Engines answer differently run to run, so a real audit runs every prompt several times and reports the variance. It covers enough prompts to map a funnel — comparison, validation, problem, alternatives — not five queries. It tests multiple engines, because they disagree. And it benchmarks competitors, because a score without share-of-voice context means nothing. A $27 scan does none of this; it prints a number.

What arrives at the end?

A report: the scorecard, share-of-voice charts, prompt-by-prompt results, flagged claims with raw evidence, technical root causes (crawler access, schema, entity consistency), and a sequenced roadmap. Ours also ships the raw data export and 90 days of an interactive dashboard — see a full sample built from real engine runs.

What does one cost?

Our June 2026 market survey maps the range: $27 automated scans, $497–2,497 thin audits covering 5–40 prompts, $950 for the CitedMetrics 50–150-prompt 4-engine audit, $4,500+ agency engagements, and monitoring tools averaging ≈$337/month forever. The honest comparison metric is depth per dollar: prompt count × engines × runs.

Who actually needs one?

Any company whose buyers research before buying — which in B2B is measurably most of them: 84% of B2B SaaS CMOs now use LLMs in vendor discovery (Wynter, 2026; the numbers live in our buyer-behavior brief). If the shortlist forms inside AI answers and you've never measured your presence there, the audit is the instrument. The free snapshot is the smallest honest version of it.

Frequently asked questions

• How is AI visibility calculated?
  As rates over repeated runs: mention rate (runs naming the brand ÷ total runs), citation rate (runs sourcing the brand's site), recommendation rate (runs endorsing it), and share of voice (the brand's mentions ÷ all brand mentions in the category). Single runs are excluded by design — engines vary, so only repeated runs measure.
• How much does an AI visibility audit cost?
  In June 2026: $27 automated scans (a few keywords, no analysis), $497–2,497 thin productized audits (5–40 prompts), $950 for our 50–150-prompt 4-engine audit, and $4,500+ for agency-grade engagements with multi-week turnarounds. Depth-per-dollar varies by an order of magnitude — always ask for prompt and run counts in writing.
• Is an audit better than a monitoring tool?
  Different jobs. A monitoring dashboard (≈$337/month category average) tracks numbers over time but leaves prompt design and interpretation to you. An audit is a diagnosis: what is true today, why, and what to fix first. The cost-rational order is audit first, then monitor the fixes.