Which tools measure a company’s visibility in AI?

The market combines at least four different tasks under one label:

1. A manual audit shows the full answer for a small number of important questions. It is best for assessing description accuracy, the nature of a mention and the reason for a recommendation.
2. Prompt monitoring periodically runs a defined set of questions. It makes it easier to observe trends, competitors and cited domains.
3. Broad-index research searches a database of answers prepared by the vendor. It helps discover topics and competitors that were not already on the list.
4. Traffic analytics records users who moved from an AI answer to the website. It is closer to a business outcome, but cannot see recommendations that ended without a click.

None of these measurements provides access to every private conversation people have with ChatGPT, Gemini or Claude. A result is a sample defined by the vendor and the study configuration.

OtterlyAI periodically checks custom prompts across several AI systems and tracks brands and cited URLs. I would choose it when I need to launch repeatable monitoring quickly for a company or agency clients. Its data collection and metric limitations are covered separately in the OtterlyAI analysis.

Peec AI also begins with prompts, but strongly organizes a marketing team’s work around visibility, brand position, sentiment, competitors and sources. I would test it when several people need to observe one category together and regularly turn findings into tasks.

Both products are more efficient than a spreadsheet for a repeatable study. A spreadsheet and manual runs retain an advantage during the first audit, when the sample changes quickly and I want to read and classify every answer myself. The manual AI visibility audit explains that process.

Ahrefs Brand Radar combines a large index of answers based on search queries with custom prompt tracking. Its strength is immediate research into any brand without waiting to build history, alongside a view of mentions and citations across search, social platforms and video. It is a strong option for discovering unknown competitors, topics and sources.

Semrush AI Visibility Toolkit is a natural extension for teams already using Semrush for SEO. It covers visibility, competitor and prompt research, cited pages, sentiment and topics. I see the most value in one workflow that moves from a competitor gap through source analysis to site auditing and content planning.

A large index gives a broader picture than a few dozen custom questions, but it introduces a different sample. A prompt connected to search volume does not prove that people ask it with the same frequency in an AI tool. Important decisions still require full answers and a custom question basket aligned with the company’s offer.

Scrunch AI combines prompt and citation monitoring with AI agent traffic analysis and features for preparing content for those agents. The vendor targets brands and agencies that require access controls, enterprise login and work across a large number of observations.

I would consider this class of solution when the problem goes beyond a marketing report. A large organization may need a shared dashboard for several markets, integrations, permissions, controls over how bots read the site and a process for implementing recommendations. For a small company starting with a dozen questions, that scope can add cost and complexity without improving the quality of the sample itself.

An AI visibility dashboard reports what appeared in the measured answers. An analytics tool such as Google Analytics 4, Matomo or a company’s product analytics system observes the next event: a user arriving from ChatGPT, Perplexity, Gemini or another referring source.

It is useful to create a separate channel for traffic from AI systems and measure valuable actions rather than sessions alone, such as a submitted form, registration or purchase. These data answer a question about people who clicked a link. They exclude users who read a recommendation and contacted the company later through another route.

Answer monitoring and traffic analytics complement one another. The first detects a change in brand exposure, while the second checks whether visibility leads to visits and actions on the site.

I would not begin by comparing feature counts. I would begin with the decision the result needs to support.

For the “30 days” project, I would use at least three layers. First, I would manually assess answer quality and refine the sample. I would then launch monitoring for the most important prompts and use a broader tool to discover topics. Finally, I would connect those data with traffic and sales enquiries. Only this combination separates brand exposure, source diagnosis and business effect.

Sources

• OtterlyAI: supported AI systems
• Peec AI: AI search analytics features
• Ahrefs: what Brand Radar is
• Semrush: AI Visibility Toolkit guide
• Scrunch AI: monitoring, citations and agent analytics
• Google Analytics: traffic source and medium