How to audit for AI search visibility
What this page covers
How to audit for AI search visibility
Audit AI search visibility by checking how your pages and hubs appear in Google and AI-powered search, then separating content, structure, and demand coverage issues.
Use Radar as a practical starting point: run a free scan to validate the output, then decide whether you need deeper access, AI interpretation, comparison, or imports for blocked sites.
In brief
- Start with a scan of the current site so you can see which pages and hubs are visible, weak, missing, or unclear in Google and AI-powered search.
- Review whether each page answers a specific search intent with useful standalone headings, practical detail, and enough context for discovery.
- Use the audit to identify whether the main issue is content quality, site structure, demand coverage, or how brand data appears across search surfaces.
What to do
A useful AI search visibility audit starts with the pages you already have. Review hubs, leaf pages, and important commercial pages, then connect visibility problems to specific URLs instead of treating search performance as one broad metric.
Next, evaluate the content structure. Remove low-value framing, identify the main intent of each section, use concrete long-tail query framing, and make headings clear enough to stand on their own outside the original page context.
Then choose the right level of tooling. The free Radar demo helps validate output with a cap of up to one thousand pages per run. Extended access adds a higher run cap, AI interpretation, two-site comparison, and JSON import for blocked or protected sites.
What to keep in mind
This audit is most useful when a US B2B growth owner needs to understand why qualified inbound demand from Google and AI-powered search is lower than expected, or why current SEO reports feel too tactical to explain structural causes.
It should not be treated as a promise that one factor will fix AI visibility. The available material is cautious about simple answers: link counts are not presented as a direct lever for LLM visibility, and files such as llms.txt are not positioned as a citation solution.
For teams delivering audits to clients, the product path separates free validation, Early Access, Design Partner use, and Enterprise needs such as integrations, security, and scaled pilots.