Berlin Tech Meetup: The Future of Relational Foundation Models, Systems, and Real-World Applications
For AI Engineers
Production predictions are only valuable if they're accurate. KumoRFM delivers better accuracy by learning from relational structure instead of flat tables. It's also pre-trained on thousands of datasets, providing additional knowledge that boosts accuracy 10-50% on RelBench. And it provides these predictions via a simple API, shipping in hours instead of months of custom integration work.
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Why AI Engineers choose Kumo
Integrate AI predictions into any product or workflow — without building ML from scratch.
API-first
REST API & Python SDK
Production-grade prediction endpoints. Plus an MCP server for agentic workflows. Integrate Kumo predictions into any product your team builds.
Explainable
Scores, not black boxes
Every prediction includes feature importance. Your product team can understand and act on why a user received a specific score.
50+
Use case templates
Churn, fraud, recommendations, LTV, demand — pre-validated prediction patterns ready to deploy. No starting from scratch.
Loved by data scientists, ML engineers & CXOs at
In production today
Enterprise Customer
Went from 3 models in production to over 50 in a single quarter with the same team. Feature engineering eliminated, pipeline complexity reduced by 95%.
Databricks
Lead-scoring models delivering dramatic improvement in conversion rates. Deployed in days instead of months, with zero feature engineering.
DoorDash
Restaurant recommendations driving hundreds of millions in GMV. Expanded to notification reranking and send-time optimization using the same foundation.
You ship AI features. Kumo gives you better predictions to ship.
Path 1 — Integrate ML outputs into production: You wrestle with latency, reliability, and data freshness. Every model requires a custom serving pipeline, feature store integration, and monitoring — before a single prediction reaches your users.
Path 2 — Use LLMs for structured predictions: They are expensive, slow, and hallucinate on tabular data. Token costs explode at scale, and you still need guardrails for every prediction type.
Kumo's API delivers explainable, production-ready predictions directly from your relational data. No feature engineering, no custom pipelines — just a REST endpoint with scores, explanations, and the reliability your product needs.
API-first
Prediction delivery
REST endpoints for every model
Real-time
Scoring
Low-latency inference at scale
Built-in
Explainability
Feature attributions per prediction
50+
Use case templates
Pre-built for common patterns
KumoRFM isn't limited to your data alone. Pre-trained on billions of relational patterns across diverse datasets and fine-tuned to your schema, it sees what no in-house model can. As per the SAP SALT benchmark.
LLM
GPT4 + AutoML
PhD Data Scientist
Feature eng. + XGBoost
KumoRFM
Relational Foundation Model
17x
increase in models shipped per quarter
“Beating internal XGBoost model on key metrics with far less data and features. We went from three models in production to over fifty in a single quarter, with the same team.”
Matt Loskamp
GTM Data Science Leader, Enterprise Customer
From startups to enterprises, leading organizations rely on Kumo to deliver predictive insights at scale.
Peer-reviewed
Kumo is built on 40+ peer-reviewed papers at NeurIPS, ICML, and KDD. The methodology is public and reproducible.
K. Huang, M. Fey, J. Leskovec et al.
A foundation model for relational data - pre-trained across schemas, it delivers accurate predictions out of the box and improves with fine-tuning on your specific data.
Read paperM. Fey, W. Hu, K. Huang, J. Leskovec et al.
Introduces learning predictive models directly on relational databases, eliminating the feature engineering pipeline that has historically bottlenecked enterprise ML.
Read paperJ. Robinson, R. Miao, K. Huang et al.
An open benchmark for evaluating relational prediction methods across 11 databases and 30 tasks. Kumo consistently outperforms traditional ML baselines.
Read paper
