AI visibility guide
AI visibility monitoring: a practical guide for brands
People increasingly ask AI assistants for products, providers, and recommendations. AI visibility monitoring gives teams a repeatable way to see whether their brand is present in those answers, how it is described, and which competitors appear instead.
What is AI visibility?
AI visibility is a brand’s presence and prominence in responses from systems such as ChatGPT, Gemini, Claude, and Perplexity. It is not a single universal ranking. A useful measurement asks a defined set of buyer questions, records the answers, and tracks change over time.
For example, a project-management company might monitor questions such as “What are the best project-management tools for a small agency?” and “What is a simple alternative to [competitor]?” The important detail is that the questions reflect genuine customer intent, not only the company’s preferred wording.
What to measure
- Inclusion: whether the brand is named at all for a relevant question.
- Prominence: whether it is among the primary recommendations or mentioned in passing.
- Positioning: the attributes, use cases, and trade-offs attached to the brand.
- Competitor context: which alternatives are repeatedly recommended and for what reasons.
- Evidence and sources: where an answer cites sources when citations are available.
How to build a monitoring routine
1. Start with buyer questions
Group questions by intent: category discovery, comparison, use case, industry, and local or regional needs where relevant. Include questions that customers use before they know your brand as well as comparisons involving known alternatives.
2. Record answers consistently
Use the same question wording, target market, and evaluation rules each time. Capture the response, named brands, ordering, and cited sources. Keep raw examples so the score can be explained later.
3. Diagnose the gap
Look for the reason a competitor is favored. Common gaps include a missing comparison page, vague product explanations, weak proof for a claimed use case, outdated third-party information, or pages that are hard for crawlers to access.
4. Improve the underlying information
Publish clear, accurate pages that answer the question directly. Support claims with first-party evidence, documentation, case studies, and credible independent references. Do not create pages solely to repeat keywords or try to manipulate model outputs.
5. Recheck after meaningful changes
Monitor on a regular cadence and compare trends by question group. Improvement may be gradual because generative systems use different sources and refresh at different times.
Where Mentioned fits
Mentioned turns this routine into a workspace: it tracks selected questions across supported AI engines, highlights competitors, and surfaces the next actions to investigate. Start with a free visibility scan to establish a baseline.
Next: read the guide to generative engine optimization.