Café Lumen takes back control of its image in AI
How a fictional Swiss coffee roaster measured its presence in ChatGPT, Claude, Gemini, Perplexity and Mistral, then tracked its progress month after month.
A fictional case built from sample data to illustrate the Periscop approach. No real client data.
A brand its customers knew, invisible to AI
Café Lumen sells specialty coffee in French-speaking Switzerland. Customers found it easily on the web, but no one knew what AI assistants answered when asked "which roaster should I choose in Switzerland?". Yet more and more buyers ask an AI that very question before searching on Google.
Measuring the invisible, without spending all day on it
Manually testing each AI, every week, across dozens of questions was not sustainable. They needed a regular measurement, comparable over time, and readable without being an expert.
A structured measurement, repeated every month
Periscop tracked the brand across the five main assistants, using a set of questions representative of the buying journey.
5 assistants queried: ChatGPT, Claude, Gemini, Perplexity, Mistral.
Real buying questions, asked identically each cycle to stay comparable.
Monthly tracking: mention rate, share of voice and tone of answers.
A clear snapshot at a glance
From the very first report, Café Lumen had a quantified benchmark and a baseline to track its progress. The figures below come from the example.
Decisions driven by data
Instead of guessing, the brand now knows where it is cited, where it is not, and which questions to work on first. Monthly tracking turns a hunch into a measurable trend, and every content action can be checked at the next cycle.
Curious where your brand stands?
Run a first measurement and get your own visibility score in AI.