What is AI brand monitoring?

Why it matters now

Search behaviour is changing. Millions of people now ask AI for recommendations instead of typing into Google. When someone asks ChatGPT "What's the best CRM for small businesses?", the brands mentioned in that response get the customer. The brands not mentioned lose without ever knowing it.

Unlike Google, where you can check your rankings and see your traffic, AI conversations are private. There's no search console for ChatGPT. No analytics dashboard for Claude. The only way to know what AI says about you is to ask it yourself — systematically and repeatedly.

How AI brand monitoring works

You write the prompts that matter to your business — the questions your customers would actually ask an AI. Then you send those prompts to multiple AI models and analyse the responses for brand mentions, competitor names, citations, and sentiment.

The key is doing this across multiple models. ChatGPT, Claude, and Gemini each have different training data and produce different recommendations. Your brand might be well-represented on one model and completely absent from another.

What you learn

• Are you mentioned? The most basic question. When people ask AI about your industry, do you appear?
• Where do you rank? Being mentioned first is different from being mentioned last.
• Who are your competitors in AI? The brands AI recommends alongside you might not be the ones you expected.
• What sources does AI cite? Some models cite URLs. See whether your content is being referenced.
• How does it change? Model updates shift recommendations. Regular monitoring catches these changes early.

How it's different from traditional brand monitoring

Traditional brand monitoring tracks mentions in news articles, social media, and review sites. AI brand monitoring tracks what happens when someone asks an AI model for a recommendation. These are different data sources with different implications.

A positive review on G2 might influence what ChatGPT says, but you can't assume it does. The only way to know is to test it directly.

Getting started

Cited Monitor makes this straightforward. Write your prompts, connect your API keys for the models you want to test, and schedule runs. Entity extraction automatically finds every brand mentioned. Share of voice shows you where you stand. And because you use your own API keys, the cost is transparent — about $0.003 per model per run.