What Kumo does
Kumo.ai is an AI platform that makes predictions directly on relational databases. Most enterprise data lives in relational databases - customers, transactions, products, interactions, support tickets - spread across dozens of interconnected tables. Traditional ML requires data scientists to manually join, flatten, and engineer features from these tables before any model can be trained. Kumo eliminates that entire process.
The platform uses graph neural networks and a relational foundation model called KumoRFM to read raw relational tables directly. It understands the relationships between tables (foreign keys), the temporal dynamics within them (timestamps), and the multi-hop patterns that connect entities across the database. The result: enterprise-grade predictions without feature engineering.
Instead of writing hundreds of lines of SQL and Python to build a feature table, a data scientist using Kumo writes a single predictive query:
The headline result: SAP SALT benchmark
The SAP SALT benchmark is an enterprise-grade evaluation where real business analysts and data scientists attempt prediction tasks on SAP enterprise data. It measures how accurately different approaches predict real business outcomes on production-quality enterprise databases with multiple related tables.
sap_salt_enterprise_benchmark
| approach | accuracy | what_it_means |
|---|---|---|
| LLM + AutoML | 63% | Language model generates features, AutoML selects model |
| PhD Data Scientist + XGBoost | 75% | Expert spends weeks hand-crafting features, tunes XGBoost |
| KumoRFM (zero-shot) | 91% | No feature engineering, no training, reads relational tables directly |
SAP SALT benchmark: KumoRFM outperforms expert data scientists by 16 percentage points and LLM+AutoML by 28 percentage points on real enterprise prediction tasks.
KumoRFM scores 91% where PhD-level data scientists with weeks of feature engineering and hand-tuned XGBoost score 75%. The 16 percentage point gap is the value of reading relational data natively instead of flattening it into a single table.
PQL Query
PREDICT churn FOR EACH customers.customer_id IN 30 days
Predictive Query Language (PQL) replaces the entire traditional ML pipeline. One query connects to your database, discovers predictive patterns across all related tables, and returns predictions. No feature engineering, no model selection, no training.
Output
| customer_id | churn_probability | confidence |
|---|---|---|
| C-1001 | 0.87 | High |
| C-1002 | 0.12 | High |
| C-1003 | 0.64 | Medium |
| C-1004 | 0.03 | High |
How Kumo is different
The difference between Kumo and traditional ML is not incremental. It is a fundamentally different approach to making predictions from relational data. The traditional ML pipeline has six stages that take weeks. Kumo collapses them into two steps that take minutes.
Traditional ML pipeline
- Join multiple tables into a flat table (days of SQL)
- Engineer features: aggregations, encodings, time windows (weeks of iteration)
- Select features: test which ones improve accuracy (days of experimentation)
- Choose a model: XGBoost, LightGBM, neural net, ensemble (days)
- Train and tune hyperparameters (hours to days)
- Deploy pipeline and maintain feature code (ongoing)
- Timeline: 4-12 weeks per prediction task
Kumo
- Connect your relational database (minutes)
- Write a predictive query in PQL (seconds)
- Get predictions - no feature engineering, no model selection (minutes)
- Timeline: minutes per prediction task
pipeline_comparison
| pipeline_stage | traditional_ML | Kumo |
|---|---|---|
| Data extraction & joining | 2-5 days of SQL | Automatic - reads tables directly |
| Feature engineering | 1-4 weeks of iteration | Automatic - discovered by KumoRFM |
| Feature selection | 2-3 days | Automatic - model learns relevance |
| Model selection | 1-2 days | Automatic - KumoRFM or fine-tuned model |
| Training & tuning | Hours to days | Minutes (fine-tuning) or zero (zero-shot) |
| Deployment & maintenance | Ongoing pipeline code | No pipeline to maintain |
| Total time to first prediction | 4-12 weeks | Minutes |
| Human expertise required | Senior data scientist | Any data analyst |
Highlighted: the total time drops from weeks to minutes, and the skill requirement drops from senior data scientist to any analyst who can write a predictive query.
KumoRFM: the world's first relational foundation model
At the core of Kumo is KumoRFM, the world's first foundation model purpose-built for relational data. Just as GPT was pre-trained on billions of words to understand language, KumoRFM was pre-trained on tens of thousands of heterogeneous relational datasets to understand the patterns that exist in structured, multi-table data.
How KumoRFM works
KumoRFM represents your database as a temporal heterogeneous graph. Each row in each table becomes a node. Each foreign key relationship becomes an edge. Timestamps are preserved as temporal attributes. A graph transformer then processes this structure, passing messages along edges to learn which cross-table, multi-hop patterns are predictive for any given task.
Because it was pre-trained on thousands of diverse relational databases, KumoRFM has already learned the universal patterns that recur across structured data: recency effects, frequency dynamics, temporal decay, graph centrality signals, and multi-hop relationship patterns. At inference time, it applies these learned patterns to your database without any task-specific training.
Zero-shot performance
On the RelBench benchmark (7 databases, 30 tasks, 103 million rows), KumoRFM achieves remarkable zero-shot performance - meaning it generates predictions on databases it has never seen, with no task-specific training:
AUROC (Area Under the Receiver Operating Characteristic curve) measures how well a model distinguishes between positive and negative outcomes. An AUROC of 50 means random guessing, 100 means perfect prediction. Moving from 65 to 77 AUROC means the model correctly ranks a true positive above a true negative 77% of the time instead of 65%.
relbench_performance
| approach | AUROC | human_effort | time_to_prediction |
|---|---|---|---|
| PhD data scientists + LightGBM | 62.44 | 12.3 hours + 878 lines of code | Days to weeks |
| KumoRFM zero-shot | 76.71 | Zero | Seconds |
| KumoRFM fine-tuned | 81.14 | Minutes of compute | Minutes |
Highlighted: KumoRFM zero-shot beats PhD-level data scientists by 14+ AUROC points with zero human effort. Fine-tuning adds another 4+ points (10-30% relative improvement).
KumoRFM 2.0: relational and tabular data
KumoRFM 2.0 extends the foundation model to support both relational and tabular data. While the original KumoRFM was designed for multi-table relational databases, KumoRFM 2.0 also handles single-table tabular datasets - making it a universal foundation model for all structured data.
This means enterprises can use a single platform for every structured data prediction task, whether the data lives in a complex relational schema with dozens of interconnected tables or in a single flat table. KumoRFM 2.0 automatically adapts its approach based on the structure of the input data.
Who built Kumo
Kumo was founded by two researchers who helped create the field of graph machine learning:
- Jure Leskovec - Stanford professor and one of the most cited researchers in graph machine learning. His research group at Stanford has produced foundational work on graph neural networks, network analysis, and relational learning. He has published 40+ peer-reviewed papers at top venues including NeurIPS, ICML, and KDD.
- Matthias Fey - Creator of PyTorch Geometric, the most widely used library for graph neural networks with 21,000+ GitHub stars. PyG is the standard toolkit used by researchers and practitioners worldwide for building GNN-based systems.
The Kumo team includes researchers and engineers from Stanford, Google, and Facebook AI. The company's work is grounded in a deep research foundation: 40+ peer-reviewed papers at NeurIPS, ICML, and KDD, and the creation of both PyTorch Geometric and RelBench (the benchmark for relational deep learning).
Use cases
Kumo supports any prediction task that can be expressed on a relational database. Enterprise customers use it across industries for tasks that traditionally required months of data science work:
use_cases
| category | example_prediction | typical_accuracy_gain |
|---|---|---|
| Churn prediction | Which customers will cancel in the next 30 days? | 20-40% improvement over manual ML |
| Fraud detection | Which transactions are fraudulent in real time? | 30-50% improvement in detection rate |
| Lead scoring | Which leads will convert to paying customers? | 25-35% improvement in conversion prediction |
| Recommendation systems | Which products should we recommend to each user? | 15-30% improvement in relevance |
| Demand forecasting | How much inventory do we need at each location? | 20-35% reduction in forecast error |
| Credit risk modeling | What is the default probability for each applicant? | 15-25% improvement in risk ranking |
| Anti-money laundering | Which transaction patterns indicate money laundering? | 30-50% reduction in false positives |
| Customer lifetime value | What is the expected revenue from each customer over 12 months? | 20-30% improvement in CLV accuracy |
Accuracy gains are based on customer results and RelBench benchmarks comparing Kumo predictions to traditional ML pipelines with manual feature engineering.
Integrations
Kumo connects directly to the data platforms enterprises already use. Data never leaves your environment - Kumo reads from your existing data warehouse and writes predictions back to it.
- Snowflake - native Snowflake app. Install directly from the Snowflake Marketplace. Kumo runs inside your Snowflake environment, reading and writing data without any external data movement.
- Databricks - lakehouse app. Integrates with Databricks Unity Catalog and Delta Lake. Predictions are written back as Delta tables.
- Google BigQuery - direct connector. Reads from BigQuery datasets and writes predictions back as BigQuery tables.
- AWS Athena - connector for querying data in S3 via Athena. Supports Parquet, ORC, and CSV formats.
- Private cloud - deploy Kumo in your own VPC for full data isolation. Supports AWS, GCP, and Azure.
Customer results
Kumo is used by enterprise customers including Fortune 500 companies. Two publicly shared results illustrate the impact:
customer_results
| customer | use_case | result | comparison |
|---|---|---|---|
| DoorDash | Prediction accuracy across ML tasks | 30% accuracy improvement | Compared to their existing in-house ML pipeline |
| Multiple prediction tasks | 4-5 years of projected work completed in 2 months | Compressed years of data science pipeline development into weeks |
Customer results are based on publicly shared case studies. Individual results vary based on data quality, database complexity, and prediction task.
The DoorDash result demonstrates the accuracy advantage of reading raw relational data versus manual feature engineering. The Reddit result demonstrates the time advantage: tasks that would have required years of data science effort - building pipelines, engineering features, training models for each use case - were completed in two months using Kumo.
Research foundation
Kumo is not a startup that applied existing technology to a new market. The company's founders created the underlying technology. The research foundation includes:
- 40+ peer-reviewed papers at NeurIPS, ICML, KDD, and other top machine learning venues. These papers cover graph neural networks, temporal graph learning, relational deep learning, and foundation models for structured data.
- PyTorch Geometric (PyG) - created by co-founder Matthias Fey. The most widely used graph neural network library with 21,000+ GitHub stars. PyG is the standard toolkit for GNN research and production systems worldwide.
- RelBench - created by the Kumo research team. The first comprehensive benchmark for machine learning on relational databases, with 7 databases, 30 tasks, and 103 million rows. RelBench provides a standardized way to compare approaches to relational prediction tasks.