LLM Shelf analyses how ChatGPT, Gemini and other AI systems answer real purchase questions — and shows whether your brand is replaced by a competitor, retailer, private-label brand or a completely different category.
AI prompts and answersWe build prompts from real user questions, search-demand signals, category queries and the client’s purchase scenarios.
Decisions, not another dashboard
Not a handful of standard KPIs, but answers to the questions that matter to a specific brand and team.
AI visibility
What matters is understanding how AI moves a user from a need, through a category, to specific brands and products.
Where the brand appears, disappears or loses.
Why the result looks the way it does.
What to do first.
Measurement tailored to the challenge
Together, we select the KPIs that answer your specific business questions.
Purchase situations
We test whether AI can move from a real need to the category, product format and brand.
Conversation journey
Users ask follow-up questions about brands, products, price, ingredients and availability. That is why we also analyse what happens after the first answer.
Audit sample
The problem was not a negative opinion of the product. AI understood the need but directed the user to other categories and competing solutions.
Brand defence
Unbranded discovery
Follow-up recovery
Negative positioning
We screen several areas and identify which ones deserve a deeper audit.
Use cases, models, follow-ups, KPIs, sources and recommendations.
We test whether implementation changes AI recommendations.
It examines how AI models guide a user from a need to categories, products, brands and retailers.
From consumer needs, search-demand data, category questions, brand communication, competitors and purchase scenarios.
Users often ask for specific brands, products, prices or availability only in the second step.
AI models are probabilistic, so larger audits repeat prompts and measure response stability.
We can start with a category, product or your most important business challenge.