Stanford professor and Kumo AI co-founder Jure Leskovec argues that his company’s tool, a relational foundation model (RFM), is a new kind of pre-trained AI that brings the “zero-shot” capabilities of large language models (LLMs) to structured databases. Kumo’s approach, “relational deep learning,” sidesteps the manual process with two key insights. First, it automatically represents any relational database as a single, interconnected graph. For example, if the database has a “users” table to record customer information and an “orders” table to record customer purchases, every row in the users table becomes a user node, every row in an orders table becomes an order node, and so on. These nodes are then automatically connected using the database’s existing relationships, such as foreign keys, creating a rich map of the entire dataset with no manual effort. Second, Kumo generalized the transformer architecture, the engine behind LLMs, to learn directly from this graph representation. Transformers excel at understanding sequences of tokens by using an “attention mechanism” to weigh the importance of different tokens in relation to each other. Kumo’s RFM applies this same attention mechanism to the graph, allowing it to learn complex patterns and relationships across multiple tables simultaneously. Leskovec compares this leap to the evolution of computer vision. RFM ingests raw database tables and lets the network discover the most predictive signals on its own without the need for manual effort. The result is a pre-trained foundation model that can perform predictive tasks on a new database instantly, what’s known as “zero-shot.” The RFM can serve as a predictive engine for these agents. Kumo’s work points to a future where enterprise AI is split into two complementary domains: LLMs for handling retrospective knowledge in unstructured text, and RFMs for predictive forecasting on structured data. By eliminating the feature engineering bottleneck, the RFM promises to put powerful ML tools into the hands of more enterprises, drastically reducing the time and cost to get from data to decision.