News
AI Visibility Monitoring Update
Kojable now gives teams a clearer view of how AI systems mention, cite, and describe their company across the topics that matter to their business.
The update moves AI visibility beyond a single score. Teams can understand where they appear, which sources support that visibility, which company pages are being cited, and where competitors may be receiving more attention.
What changed
Kojable now monitors AI visibility at the answer and citation level
Companies increasingly need to understand how they are represented inside AI-generated answers—not only whether their name appears.
Kojable’s AI Visibility Monitoring helps teams examine the answers associated with their tracked topics, identify the sources cited in those answers, and understand how much of that visibility belongs to their company, competitors, and other influential domains.
Answer: teams can see where their company is present in AI answers, whether their own pages are being cited, and which topics or sources may require attention.
Why it matters
AI visibility can influence discovery before a buyer reaches your website
AI systems are becoming part of how people research categories, compare vendors, investigate problems, and decide which companies deserve further consideration.
A company can have strong traditional search visibility and still be missing, weakly represented, or supported by the wrong evidence inside an AI-generated answer.
Being mentioned is not the same as being supported
An AI answer may name a company without citing one of its pages or relying on evidence that accurately represents it.
Citations reveal where the answer is coming from
The sources associated with an answer help teams understand which websites, publications, communities, competitors, and company pages are shaping the result.
Visibility differs by topic
A company may be well represented for one product area but absent from another category, use case, or buyer question.
A single percentage can hide important context
Sample size, market support, platform coverage, and collection completeness all affect how a visibility result should be interpreted.
What teams can measure
A more complete view of AI visibility
Kojable combines several measurements so teams can understand both presence and supporting evidence.
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Citation Share of Voice
The proportion of observed citation activity that points to the company’s owned websites and pages.
It helps answer: How much of the cited evidence belongs to us?
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Citation coverage
The proportion of relevant AI answers that contain at least one citation to an owned company page.
It helps answer: How often are our pages supporting the answers in which we should appear?
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Answer mention rate
The proportion of relevant answers that name the company, an approved brand name, or an owned domain.
It helps answer: How consistently is our company included in the answer itself?
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AI opportunity-weighted coverage
Citation coverage considered alongside the estimated demand associated with the underlying questions.
It helps answer: Are we visible for the questions that may carry the greatest opportunity?
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Distinct owned URLs cited
The number of different company pages receiving citations.
It helps answer: Is AI visibility concentrated on one page, or supported across a broader set of useful company content?
Go beyond the headline metrics
Understand the topics, platforms, models, and sources behind visibility
Portfolio-level measurements are useful, but they should not become a black box.
Kojable helps teams investigate the underlying visibility observations so they can understand:
- Which tracked topics produce relevant AI answers
- Which questions mention the company
- Which company pages receive citations
- Which competitor domains receive attention
- Which publications, authorities, communities, and marketplaces appear as sources
- How visibility differs across available platforms and models
- When a citation was first or most recently observed
- Whether the available evidence is sufficient or still directional
The goal is to help teams move from a vague visibility number to a practical explanation of what is happening and what deserves investigation.
Data confidence
Sparse or unavailable data should not look like poor performance
A missing observation is not automatically a zero.
AI-answer coverage can differ by market, language, platform, model, topic, and collection period. Some topics may have substantial evidence, while others may have only a small number of relevant answers or no supported observations yet.
Kojable distinguishes between states such as:
- Sufficient evidence
- Directional evidence
- Very sparse evidence
- No indexed responses
- No relevant responses
- Unsupported coverage
- Partial collection
- Setup required
Percentages should be accompanied by their underlying sample, and incomplete collections should not create false conclusions about lost visibility.
This helps teams stay grounded in what the data can support rather than treating every missing observation as a performance failure.
What comes next
Build a reliable baseline for AI representation
The first collection establishes a baseline for the company’s tracked topics.
As comparable observations accumulate, teams will also be able to understand what has changed, including:
- New company citations
- Lost company citations
- New competitor citations
- Lost competitor citations
- Topics gaining visibility
- Topics losing visibility
- Changes across platforms and models
This gives teams a way to monitor AI representation over time instead of relying on occasional manual checks.
Next step
See what is shaping AI’s answers about your company
Identify where your company is visible, which sources support that visibility, and which gaps deserve attention.