AI shopping is becoming a recommendation surface with its own taste, shortcuts, and failure modes.
How AI shopping systems decide what gets recommended.
We study how models behave, where they drift, and what brands need to change to be accurately represented in those machine-generated journeys.
AI shopping is a recommendation surface with its own taste.
Product data quality now affects not just feeds and search, but model-generated answers.
Model visibility is measurable.
Research built for the AI recommendation era.
The Genesis studies map how models perceive fashion, which aesthetics they privilege, and how those preferences leak into what they recommend back to consumers.
Testing AI vision on high-fashion nuance.
A foundational benchmark comparing CLIP, SigLIP, and DINOv2 across runway images, uncertainty detection, and collection coherence.
Tracing what LLMs repeatedly choose in fashion.
A multi-model look at aesthetic consensus, canonical fashion objects, and how preference clusters emerge across current AI systems.
Research becomes action when your products need to show up.
The second half of Caeliai is applied: audits, feed correction, AI visibility strategy, and instrumentation for understanding which systems are naming you and how.
Check how major AI systems represent your products, pricing, and links.
Fix weak product data before it compounds into agent mistakes.
Tie AI-originated discovery back to commercial outcomes instead of treating recommendation surfaces like black boxes.