Visibility outcomes
AI visibility audit for mentions, entities, and citation coverage
This page is about visibility outcomes — whether AI answers mention your brand, understand your entities, and cite related sources — not a second copy of the page-structure AEO audit explainer.
What a visibility audit examines
Brand mention presence
For priority prompts (category, alternative-to, “best tools for…”), is your brand named, paraphrased, or omitted?
Entity and topic understanding
Does the answer associate you with the right product category, features, and use cases — or confuse you with neighbors?
Citation and source coverage
When sources appear, which domains are referenced? Are your pages among them, or only third parties?
Competitor visibility gaps
Which competitors are recommended instead, and on which query themes do they appear more often?
How visibility is measured and interpreted
Visibility analysis usually combines prompt sets, observed answers, and content diagnostics. Amisora’s page audit helps explain why content may be hard to cite; visibility review asks what appears in answers for your market.
- Define a fixed query set (branded, category, comparison, use-case).
- Record whether you are mentioned, how you are described, and which sources are shown.
- Map omissions back to content and entity weaknesses on your site.
- Re-test after edits — visibility can lag and varies by engine.
For ongoing tracking concepts, see the LLM visibility tool page. For brand-level monitoring ideas, see AI brand monitoring.
Content and entity weaknesses that reduce visibility
- Missing one-line definitions — engines struggle to place you in a category.
- Thin comparison pages — “alternative to” queries favor clearer tables and criteria.
- Inconsistent naming — product, legal, and brand names diverge across pages.
- Unsourced claims — answers prefer passages that look attributable.
Improve extractability with the LLM-extractable content guide, then run an AEO audit on priority URLs.