How Genezio Measures Visibility
Genezio measures AI Visibility by analyzing how often a brand appears in answers generated by AI systems during simulated conversations. The most important signal Genezio tracks is AI Recommendations — whether the brand is actively recommended as a solution by answer engines. AI Visibility measures the broader presence: how often your brand appears in AI answers at all, whether as a mention, comparison, or recommendation.
At its core, the visibility metric answers a simple question:
In how many AI conversations does your brand appear?
Visibility is therefore expressed as a percentage of conversations where the brand is mentioned.
The Visibility Formula
AI Visibility is calculated as:
Visibility = (Conversations where the brand appears / Total eligible conversations) x 100If a brand appears in 42 out of 100 conversations analyzed by Genezio, its AI Visibility for that set of scenarios is:
42%This percentage provides a clear view of how frequently the brand appears when AI systems answer questions related to a specific topic.
What Counts as a Brand Appearance
A brand is considered visible in a conversation when the AI assistant:
- explicitly mentions the brand
- includes the brand in a recommendation list
- compares the brand with competitors
- references the brand as part of an explanation
The brand does not need to be the main focus of the answer. Any clear mention of the brand counts as visibility for that conversation.
Genezio Agent Types
Genezio runs different types of conversations to simulate realistic interactions with AI systems.
These Genezio Agent types represent different user intents.
Prompter Agent
A Prompter Agent conversation simulates a user asking an open-ended question about a category.
Example:
User query: What CRM should a startup use?
The AI assistant responds by recommending or listing relevant products.
Prompter Agent conversations are one of the primary signals used to measure visibility because they reflect how brands appear when users ask general discovery questions.
Recommender Agent
A Recommender Agent conversation simulates a user explicitly asking the AI to recommend solutions.
Example:
User query: Recommend project management tools for small teams.
These conversations are also used to calculate visibility because they reveal which brands AI systems recommend in response to common requests.
Introspector Agent
An Introspector Agent conversation asks the AI system about a specific brand.
Example:
User query: What is HubSpot used for?
Because the brand name is included in the question, the AI assistant will almost always mention the brand in its answer.
For this reason, Introspector Agent conversations are not used when calculating AI Visibility.
Including them would artificially inflate the visibility score.
Comparer Agent
A Comparer Agent conversation asks the AI system to compare specific brands.
Example:
User query: HubSpot vs Pipedrive: which CRM is better for startups?
In this type of conversation, the brands appear in the prompt itself.
Because the model will almost always reference those brands in the response, Comparer Agent conversations are excluded from KPI calculations. The Comparer is a competitive analysis tool — it uses your brand-level metrics to show how you stack up against specific rivals, but it does not contribute to the calculation of AI Visibility or AI Recommendations.
Which Agent Types Generate KPIs
| Agent | Generates KPIs? | Role |
|---|---|---|
| Prompter | ✓ AI Visibility | Open-ended discovery — AI chooses which brands to mention |
| Recommender | ✓ AI Recommendations + Visibility | Scenario-driven — AI chooses which brands to recommend |
| Introspector | — | Narrative analysis — explores how AI describes your brand |
| Comparer | — | Competitive analysis — compares your brand against specific rivals |
Both KPIs are brand-level metrics generated from Prompter and Recommender conversations, where the AI decides on its own which brands to surface. This ensures the metrics reflect organic brand presence and genuine recommendation decisions.
Visibility Across Topics
Visibility is typically measured within the context of a specific topic or scenario set.
For example, a brand might have:
- 60% visibility for "CRM for startups"
- 35% visibility for "sales tools for small businesses"
- 10% visibility for "marketing automation platforms"
This helps organizations identify where their brand is strongly associated with a topic and where visibility is limited.
Why Visibility Is a Useful Metric
AI Visibility provides a high-level signal about how AI systems perceive a brand within a category.
A higher visibility score suggests that:
- the brand is frequently associated with the topic
- the brand appears in recommendations
- the brand is commonly referenced in the sources used by AI systems
A lower score may indicate that competitors dominate the conversation or that the brand is underrepresented in the information sources used by AI models.
Next Steps
To understand how visibility compares with competitors, continue with:
These pages explain how Genezio expands visibility analysis into deeper competitive insights.