Berlin Tech Meetup: The Future of Relational Foundation Models, Systems, and Real-World Applications
For VPs & Heads of Engineering
Your ML pipelines flatten data and cap accuracy. KumoRFM learns from relational structure directly, and it's pre-trained on thousands of datasets, adding accuracy your custom models can't reach. It also replaces months of pipeline engineering with hours. Fewer pipelines, higher accuracy, faster delivery, with zero new infrastructure.
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Why Engineering Leaders choose Kumo
Here's exactly how Kumo changes the operating model of your engineering organization.
95%
Reduction in ML pipeline code
No custom ETL per model. No feature store to maintain. No per-model serving infrastructure. Kumo connects to your existing data warehouse.
SOC 2 Type II
Enterprise-grade from day one
Private cloud deployment, SSO, audit logs, role-based access. Your security team will approve this faster than an internal build.
50+
Use cases on one platform
Your engineering team maintains one integration instead of dozens. Fraud, churn, recs, LTV, demand — all from the same 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 — ML infrastructure consumes 80% of engineering bandwidth: Feature stores, training pipelines, serving infrastructure, monitoring — your platform team spends most of their time keeping the lights on instead of building new capabilities. Every new model adds weeks of integration work.
Path 2 — Each new model adds maintenance burden: Custom ETL per use case. Per-model feature pipelines. Bespoke serving endpoints. Your on-call rotation grows with every model, and your best engineers are debugging data drift instead of shipping features.
Kumo replaces the entire ML stack. It connects directly to your data warehouse, handles feature engineering, training, and serving — all from one platform. Your engineers maintain one integration instead of dozens, and your team gets back to building products.
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
