Research
Data-driven analyses of AI visibility
We examine how brands become visible in AI systems. Our research publications provide robust data — rather than speculation — across industries, regions, and models.
Our research areas
Here, we publish data-driven analyses and research papers on AI visibility, AIVA, and brand visibility in AI systems. Each publication is based on systematically collected data, not on isolated observations.
In our studies, we analyze the sources used by AI systems such as ChatGPT, Google AI Overviews, Gemini, and Perplexity, the brands recommended, and how visibility varies across industries, regions, funnel stages, and models. This provides a reliable basis for decision-making in marketing and corporate management.
State of AI Search 2026: LLM Visibility for Brands
11,000 Prompts · 11 Industries · DACH and USA · 6 AI Models
Large language models are fundamentally transforming search. Instead of a list of results, AI systems provide direct answers, including sources and brand recommendations. This study offers the first systematic analysis of how brands appear in this new ecosystem and what this means for their visibility.
From the study:
- AI search is a standalone ecosystem. A website alone is not enough.
- Industries follow five distinct archetypes. There is no one-size-fits-all LLM strategy.
- The brand landscape is stable. Established brands consistently reappear, while new ones struggle to break in without a strategy.
A study by AN Digital in collaboration with Peec AI.
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