This is where you can add individual terms/keywords to be tracked on various AI Search Engines. You can also set the frequency of the updates for those terms.

You have 2 ways of adding terms to your brand project. You can add manually by clicking on “+ Add Term” or by using the AI to “Find Terms”, based on a topic.

Click on “+ Add Term”, select the topic (optional) , enter your custom search term, choose which AI Search engines you want to track (bear in mind that each uses different amounts of credits per run), and then add the frequency that you want to have the term updated. You can also add a description if necessary.

If you are tracking ChatGPT or AI Overviews, you can select the location for the search.

There is no limit to the number of characters you can enter for the search term.

You can use the “Bulk Add Terms” to add multiple terms with different AI Engines, frequency and location.

If you are not sure on what terms to track, you can use the “Find Terms” option that uses AI to give you suggestions, based on your topic.

From the suggestions you can choose which terms you want to add, which AI Search Engines to track and the frequency. If you are tracking ChatGPT or AI Overviews, you can select the location for the search.

Once you have added your terms you can see them in the Search Terms list. From the list you can edit, copy or delete the terms.

From the search term table you can see the following information per term

If you expand the Term, you will metrics:

To view the detailed metrics and performance over time click on the “View Results”.

From the table you can also select terms and then export all the raw data to csv or Google Sheets.

After clicking the “View Detailed Results” you will see the detailed metrics, history and overall performance of that specific term for that specific AI Engine. It provides a detailed breakdown of how your brand is appearing in AI-generated search results for a specific search query. It gives you insights into your visibility, how you’re being positioned, and how often you’re mentioned or referenced alongside competitors.

At the top of the page, you’ll see the exact search query being analyzed, along with the brand, AI model (e.g., Google Gemini 2.0), region, and total test runs used to generate this report. This gives you context on what query was tested, how often, and when the last run occurred.

This section summarizes your brand’s performance across the selected test period (usually the last 7 days). It includes:

This table shows a side-by-side comparison of all brands detected in the AI-generated results. For each brand, you can see:

This helps you understand how you’re performing against competitors in terms of presence, tone, and placement in AI-driven content.

This section shows which brands were referenced together in the AI response and how often. It highlights relationships between your brand and competitors — for example, if your brand was cited in comparison to others, or mentioned in the same context.

You’ll also see how often each brand was cited and the date range those citations were found.

The execution history logs each test run — including the AI model used, the brands detected in that run, and whether your brand appeared. From here, you can:

This section provides transparency into the raw output from each AI run, so you can verify how your brand is being picked up.

The AI Result Snapshot (aka. AI Spyglass) lets you view the actual AI-generated search result from a specific test run. This is similar to our Spyglass feature for organic tracking — it gives you full transparency into how your brand (and others) were mentioned in the response, helping you validate the data behind the visibility metrics.

What you’ll see in the snapshot:

To access this you go to the detail view of the term and then from the Execution History section, click on “View Results” next to a specific run to open the AI Result Snapshot.