Discover a new benchmark designed for real-world impact. Built on authentic private-company data and carefully chosen public datasets that reflect real industry challenges, like product categorization, basket prediction, and personalized recommendations, it offers a realistic testing ground for both classic baselines (e.g., gradient boosting) and the latest models such as CARTE, TabICL, and TabPFN. By bridging the gap between academic research and industrial needs, this benchmark brings model evaluation closer to the decisions and constraints faced in practice.

This shift has tangible consequences: models are tested on problems that matter to businesses, using metrics that reflect real-world priorities (e.g., Precision@K, Recall@K, MAP@K). It enables more relevant model selection, highlights where academic approaches fall short, and fosters solutions that are not just novel but deployable. Models are judged on tasks and metrics that matter, enabling more informed choices, exposing the limits of lab-only approaches, and helping accelerate the journey from innovation to deployment.