AI Visibility Audit

An AI visibility audit is a structured review of whether, where and how a brand appears in AI-generated answers. It tests commercially relevant buyer prompts, compares competitors, inspects cited sources and identifies inaccurate or missing brand associations that deserve action.

What does an AI visibility audit reveal?

The audit establishes a baseline for AI visibility: mentions, recommendations, citations and the way platforms describe the brand. These are different outcomes. A brand may be mentioned but not recommended, cited without being named prominently or described using outdated positioning.

The review should also identify which topics and buying stages produce visibility. That prevents a broad informational mention from being treated as equivalent to inclusion in a supplier shortlist.

How do you run the audit?

Define the audiences, services and attributes the brand should be associated with. Build a fixed prompt set from real buyer questions, then test it across the platforms the audience uses. A practical AI visibility audit process covers problem research, category discovery, comparisons, recommendations and branded questions.

Record the full answer, date, platform, brand and competitor appearances, cited URLs and factual errors. Repeat high-priority prompts because generated answers can vary.

Which metrics matter?

Track mention rate, recommendation rate, citation rate, source ownership, narrative accuracy and competitor presence separately. An AI-focused share of voice can compare how often selected brands appear across the same prompt set.

Segment results by platform, topic and buyer stage. One blended score can hide the difference between educational visibility and commercial consideration. Add qualified referral traffic, self-reported discovery and influenced pipeline where reliable data is available.

How should cited sources be assessed?

For every important answer, inspect which webpages support it and whether the brand’s own site is included. This shows whether the gap sits in website content, third-party coverage or retrieval. A citation is evidence that a source was used, not proof that the system prefers or recommends the brand.

Prioritize recurring source patterns. One isolated answer is weaker evidence than the same omission, inaccurate claim or competitor source appearing across repeated tests.

Why does an audit matter for AI Search?

An audit turns AI Search from an assumption into a measurable diagnosis. It shows where buyers may encounter the brand, what information shapes the answer and which gaps are commercially important. A structured AI Search optimization programme can then connect each finding to one of three actions: fix unclear information, build a missing source page or influence relevant third-party sources.

The audit should end with ranked actions, owners and a stable benchmark for retesting—not a vanity score without context.