Search isn’t just “10 blue links” anymore. A big chunk of discovery is moving to AI answers: people type a question, the model replies with a shortlist of tools, and that shortlist quietly becomes the market map.

If someone asks “best reporting tools” or “best marketing automation platform” and your product doesn’t appear, you won’t even get considered. And the annoying part is that you can’t easily tell if you’re showing up unless you manually test dozens of prompts across multiple models, then repeat it every week because answers change all the time.

AI Brand Mention Tracker (AEO / GEO) solves that. It runs your keyword prompts on a schedule, checks multiple AI models, and logs the results to Google Sheets so you can track visibility over time — the same way you’d track rankings in Google Search Console, but for AI answers.

What the template actually does

You add a list of prompts (keywords) in a Google Sheet. These should be things your customers would realistically ask AI in your category. For each prompt, you also enter the brand you want to track and the domain you want to detect, because a mention plus an official domain is often the strongest “citation” signal.

When you run the workflow, it will:

• read each prompt from the sheet
• query your selected AI model(s)
• capture the raw response
• detect whether your brand name appears in the response (including simple variations like spacing/case)
• detect whether your domain/link appears in the response
• append a clean result row back into Google Sheets

The output becomes your baseline: which prompts you appear for, which models mention you, and whether you’re getting linked.

Why this matters for AEO and GEO

AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) are basically the same idea: instead of ranking as a web page, you’re trying to become the brand the “answer engine” recommends.

This tracker gives you a way to measure that without guessing:

• Are we being mentioned for the prompts we care about?
• Are we being recommended consistently, or only sometimes?
• Do we get an actual domain mention, or only a name-drop?
• Which prompts are “wins” vs “blind spots” we need content for?

What you’ll see in the Results tab

Each run logs one row per prompt × model, typically including:

• prompt / keyword
• model name
• brand mentioned (yes/no)
• domain/link present (yes/no)
• timestamp
• response text (optional, but useful for verification and debugging)

Once you have a week or two of runs, you can start charting simple metrics like “% of prompts where we’re mentioned” or “link rate,” and you’ll quickly spot patterns — certain categories where you show up, and certain query types where competitors dominate.

Common ways people use this

• Track AI visibility weekly the same way you track SEO rankings.
• Compare competitors by adding multiple brands per prompt and watching share-of-voice.
• Validate content launches: publish a page or a comparison post, then see if mentions improve over the next weeks.
• Catch model shifts: AI answers change frequently; this helps you detect drops before you notice it in pipeline.
• Build an agency deliverable: an “AI Visibility Audit” report is basically this data + insights.

Prompts that usually work best (high-intent patterns)

If you want signal (brand/tool lists), these are the patterns that most often trigger recommendations:

• “best [category] tools”
• “top [category] software”
• “[category] reporting tools”
• “[brand] alternatives”
• “[brand] vs [competitor]”
• “what tool should I use for [task]”
• “how to [task] + recommended tools”
• “checklist for [task]” / “template for [task]”

Start with 10–30 prompts, verify the output quality, then scale up once you’re confident the tracker is giving you stable, comparable results.

Who this template is for

SaaS founders, SEO/growth teams, and agencies who want a simple, repeatable way to answer: “Do AI answers recommend us?” — and to track that as a real metric over time, not a one-off anecdote.