Kumo provides the first foundation model built specifically for structured business data stored in relational databases and data warehouses. The platform enables organizations to generate predictive insights without building traditional machine learning pipelines or performing extensive feature engineering.

The system connects directly to existing data warehouses and allows users to interact with pretrained tabular foundation models through natural language queries. Business users can ask predictive questions without coding or ML expertise, receiving explainable answers backed by the foundation model's understanding of relational data patterns. For more advanced needs, teams can fine-tune custom models on their specific data while maintaining enterprise-grade governance and security controls.

Kumo's architecture processes relational data through graph transformers that understand the connections and relationships within enterprise databases. The platform generates predictions that can be pushed directly to operational tools and platforms through reverse ETL, enabling real-time actions based on model outputs. All predictions include transparent reasoning and explainability features to support decision-making and compliance requirements.

The platform serves use cases across retail operations, financial risk management, media personalization, and customer engagement. Organizations use Kumo to predict customer lifetime value, detect fraudulent transactions, optimize advertising performance, forecast churn, and deliver personalized recommendations at scale.