We help your brand get recommended more often by AI.

This is what AI models actually know about you and what meaning they've attached to your brand. Which words and concepts does the model link to your name? In which category do they see you? Ask ChatGPT about Coca-Cola and you get a rich answer immediately, built from years of mentions and context. Ask it about an unknown brand and the model has to start searching first to say anything meaningful. Those are two different starting positions, each requiring its own approach. We test this with multiple models, without letting the model look up live, so we see what's actually in the model's knowledge. And where the gap sits between your own positioning and what AI has made of it over the years.

YouTube, Reddit, LinkedIn, trade media, podcasts, forums, reviews. These are the places where AI builds its picture of your brand. We measure per platform how often you're mentioned, in what context, how that compares to the competition, and where the gaps are. Not as a stand-alone sentiment score, but as a signal for what AI will say about you in its answers. This is the slow, stubborn layer of AI Search, and at the same time the layer with the biggest leverage.

The topic universe is the playing field where AI ultimately makes recommendations. We map every topic discussed in your category, not just what shows up in your own content. Per topic we measure strategic weight and competitive intensity, and we see where you stand against the most important competitors. That immediately surfaces the topics where coverage is still low, and where you can win ground faster than on the heavily contested ones. This forms the basis of the content strategy and decides which topics we actively monitor in the layer below.

This is what the market now calls prompt tracking, and here we do it materially differently. Most tools ask a prompt once and present that one measurement as the truth. AI answers are variable, so that's the same as rolling a die once and thinking you know the distribution. We ask every question as often as needed to say what the signal is with statistical confidence, and we tune that number to the variance in the answers. Every metric gets a confidence label: robust, adequate or minimal. No noise sold as advice.