A growing database of experiments, frameworks, and research exploring how AI recommendation systems influence ecommerce discovery, brand visibility, and buying behaviour.
Search PickMeLabs research by concept, buying context, category, or recommendation pattern.
If you're new to PickMeLabs, these are the best places to understand how AI recommendation systems work, the frameworks we use to study them, and how our experiments are designed.
Foundational concepts for understanding how AI systems retrieve, interpret, and recommend ecommerce brands across different buying environments.
Explore AI Basics →The proprietary frameworks used throughout our research — AI Visibility, Recommendation Eligibility, Category Anchors, Buying Context, and Reinforcement Ecosystems.
Explore Concepts →How PickMeLabs structures experiments, interprets recommendation variability, and studies AI systems across different buying environments.
View Methodology →Long-form analysis exploring recurring AI recommendation behaviour, brand visibility patterns, and how ecommerce discovery shifts across AI buying environments.
Understanding how AI systems surface products and companies in modern buying conversations — and why recommendation patterns differ from traditional search rankings.
Exploring why broad awareness doesn't always translate into AI recommendation visibility — and what contextual clarity has to do with it.
Understanding why some brands surface consistently across AI recommendation environments while others appear only in constrained contexts.
How AI recommendation inclusion differs from search engine rankings — and why traditional SEO strategies don't always translate.
Examining how query framing, buyer constraints, and contextual signals influence which brands AI systems surface in recommendation environments.
Controlled ecommerce recommendation experiments run across AI systems to observe how brands surface, disappear, and repeat across buying environments.
How AI recommendation systems retrieve and interpret information
Proprietary frameworks for studying AI visibility and recommendation behaviour
Long-form research exploring observed recommendation patterns
Controlled studies generating the underlying recommendation data
AI Basics explain how recommendation systems work. The Concept System provides the framework language used to study them. Research Notes analyze observed patterns. Experiments generate the underlying observations. Together, they form a structured research ecosystem exploring how AI recommendation systems choose ecommerce brands.
PickMeLabs helps ecommerce brands study how they surface across AI-generated buying environments — including recommendation patterns, competitor visibility, and shifting discovery behaviour across systems like ChatGPT, Claude, Gemini, and Perplexity.