Tabular Foundation Models for Enterprise Use Cases

If you've ever applied for a credit card, had your insurance premium calculated, or seen a "customers also bought" recommendation, you've been touched by gradient boosted decision trees. These algorithms—with names like XGBoost, LightGBM, and CatBoost—have been the undisputed rulers of tabular machine learning for over a decade. Before we get into the advantages of tabular foundation models, let's understand why these traditional methods have worked so well.

Foundation models—the technology behind ChatGPT and image generators—are now being built specifically for the rows and columns of your business data. This series explores how they work, their value for enterprises, limitations, and future developments in the field.

Discover how retailers can transform product categorization from a tedious task into a strategic driver of sales.

Introducing TabBench, an open-source benchmark built by Neuralk-AI to evaluate tabular ML models on practical, real-world industry tasks, starting with commerce-related use cases.

Discover how Tabular AI can outperform classical ML with instant predictions and minimal manual effort.

We put our Tabular Foundation Model head-to-head against the recently released LLM-based classifier from Mistral AI, focusing on Product Categorization.