Lexsi Labs is building the next generation of Tabular Foundation Models (TFMs) to make structured prediction as reusable and scalable as foundation models made language and vision.

We are already deep in this space with concrete systems and research artifacts:

• We built TabTune , a unified library for inference, benchmarking, evaluation (including calibration and fairness), and fine-tuning across multiple leading TFMs through a single API.
• We developed frontier architectures like Orion-MSP , introducing hierarchical multi-scale sparse attention and memory mechanisms designed to scale tabular in-context learning to wide, high-dimensional tables.
• We are pushing the ecosystem toward Institutional Tabular Foundation Models (ITFMs) , arguing for table-native predictors that capture institutional knowledge directly, without routing tables through...