How to Track SaaS Tool Mentions in AI Assistants

To track SaaS tool mentions in AI assistants, build a fixed list of buyer prompts, run them regularly across the main assistants, and record not just whether your tool is named but how: recommended, merely listed, compared, or described wrongly. Over time this shows whether your visibility is growing and which content is helping.

Not all mentions are equal

A simple “yes, we were mentioned” hides a lot. For software, it helps to log each mention as one of these types:

• Top recommendation: the assistant names your tool first or says it is a strong fit for the user’s case.
• Listed option: you appear in a list of five or more tools with little detail.
• Comparison mention: you appear as the alternative to a bigger competitor.
• Cited source: your website or docs are linked as a source, even if your product is not recommended.
• Inaccurate mention: you are named, but with wrong pricing, features or audience.

An inaccurate mention can be worse than no mention, so give it its own column.

Building a prompt set that reflects real buyers

Your tracking is only as good as your prompts. Take them from real sources:

1. Phrases prospects use in demo requests and sales calls.
2. Search terms from your Google Search Console that show buying intent.
3. Questions in your support inbox before people sign up.
4. Category labels used on software review sites.

Example prompts for a project management tool might be: “simple project tool for a 5-person design studio”, “Trello alternative with time tracking”, “is [your tool] good for client approvals?”. Keep the wording natural, as a person would type it.

How to run the checks

AI answers vary from one run to another, and from one user to another. To get a fair picture:

• Test each prompt in several assistants: ChatGPT, Gemini, Claude, Perplexity and Copilot.
• Run each prompt more than once, and note how often you appear rather than a single yes or no.
• Use a clean session where possible, so past chats do not bias the answer.
• Record the date, because answers change as models and web results update.

Reading the results

Once you have a few months of data, look for patterns:

• Strong in one assistant, weak in another: often a sign that one assistant relies on live web search while another leans on older training data. Check where your newer content is indexed.
• Cited but not recommended: your docs are useful, but your positioning is unclear. Strengthen use-case pages.
• Named only in comparisons: you are known as “the alternative”. Build pages that show your own strengths for specific audiences.
• Old facts repeated: update pricing and feature pages and the third-party listings that copy them.

Linking mentions to business results

Mentions are a leading signal, not revenue. Connect them to outcomes by adding “AI assistant” to your sign-up source field, watching referral traffic from tools that send clicks, and asking new customers how they found you. If you want more on the measurement side, read how to measure AI visibility and our page on tools for LLM citation tracking.

How Keboost helps

Doing this by hand takes hours each month. Keboost automates the tracking: it runs your SaaS prompts across the main AI assistants, shows where your tool and its competitors appear, and keeps a history so you can see trends. It also creates and distributes focused content to fill the gaps it finds. It does not promise mentions. Run a free AI visibility check to see your first results.