LimiX is a foundation model designed for tabular and structured data tasks, replacing bespoke ML pipelines with a single model architecture. It uses a transformer encoder that embeds both features and targets as tokens, applying cross-attention across sample and feature dimensions to identify salient patterns. The model supports diverse predictive tasks including classification, regression, missing-value imputation, feature selection, and causal inference through dedicated output heads.