Berlin Tech Meetup: The Future of Relational Foundation Models, Systems, and Real-World Applications
For Chief Operating Officers
Operational predictions need both accuracy and speed. Traditional ML flattens relational data into snapshots, losing the connections between orders, customers, regions, and supply chains. KumoRFM learns from that relational structure directly, capturing signals flat tables never could. It's also pre-trained on thousands of proprietary and public relational datasets, bringing pattern knowledge no in-house model has seen and delivering an additional 10-50% accuracy boost (proven on Stanford RelBench). And where each operational model takes months of pipeline work, KumoRFM delivers production predictions in hours across demand, fraud, churn, and every use case.
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Why COOs choose Kumo
Here's exactly how Kumo transforms operational decision-making across your organization.
$100M+
Revenue impact proven
DoorDash drives hundreds of millions in GMV from Kumo-powered recommendations. Your operations data holds similar untapped value.
20x
Faster operational predictions
Demand forecasting, inventory optimization, fraud detection — from months of pipeline work to production in hours.
1 Platform
For every operational use case
Replace fragmented point solutions — fraud, churn, demand, pricing — with one platform that learns from all your relational data.
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 — Separate pipelines for every prediction: Each operational prediction — demand forecasting, fraud detection, churn prevention — requires its own ML pipeline and dedicated team. The result: slow deployments, ballooning costs, and an ever-growing backlog of operational questions that never get answered.
Path 2 — Generic AI tools: They don't understand the relationships in your operational data. Orders connect to customers, customers connect to regions, regions connect to supply chains — generic tools flatten this structure and lose the signal that matters most.
KumoRFM sees across all your tables and delivers predictions for every operational question — demand, fraud, churn, recommendations — from one platform that understands your relational data natively.
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
