Berlin Tech Meetup: The Future of Relational Foundation Models, Systems, and Real-World Applications
For VPs & SVPs of Machine Learning
Your custom pipelines flatten relational data, capping model accuracy. KumoRFM learns from the full relational graph, and it's pre-trained on thousands of datasets your pipelines have never seen, delivering an additional 10-50% accuracy boost. It also eliminates months of feature engineering, shipping production models in hours. Better accuracy, less infrastructure, faster delivery.
Book a demo and get a free trial of the full platform: research agent, fine-tune capabilities, and forward-deployed engineer support.
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Why ML Leaders choose Kumo
Stop losing your best ML talent to infrastructure work. Here's what changes when you remove the plumbing.
95%
Less pipeline code
No feature stores, no custom ETL, no per-model serving infrastructure. Kumo connects to your warehouse and handles the entire ML lifecycle.
Hours
Not months to production
Define a prediction in Kumo's query language. Get a production-ready model with evaluation metrics, explainability, and continuous retraining — in hours.
17x
More models shipped
One ML organization went from 3 production models to 50+ in a single quarter. Same team. No burnout. Just a better platform.
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.
The challenge you know too well
Path 1 — Build it yourself: Each model needs custom ETL, feature engineering, serving infrastructure, and monitoring. 80% of your team's time is plumbing. Your best researchers spend more time debugging Airflow DAGs than improving model accuracy.
Path 2 — Try LLMs for structured data: LLMs can't handle structured relational data at production accuracy. They tokenize your tables as text, losing the relational structure — primary keys, foreign keys, temporal patterns — that encodes your most valuable signals.
Kumo replaces the entire pipeline. It connects to your data warehouse, learns directly from relational structure, and delivers production-ready predictions — so your ML team can finally focus on modeling, not infrastructure.
95%
Less data preparation
Feature engineering eliminated
10–50%
Accuracy improvement
Over traditional ML (RelBench)
20x
Faster time-to-value
From months per model to hours
55+
Use cases
Validated in production
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
