Berlin Tech Meetup: The Future of Relational Foundation Models, Systems, and Real-World Applications
For VPs & Heads of Product
AI-powered product features are only as good as the predictions behind them. Your current models flatten relational data, limiting accuracy. KumoRFM learns from that structure directly, and it's pre-trained on thousands of datasets, adding an accuracy boost your product has never had. It also ships prediction APIs in hours, not the 6-month ML engineering queue your roadmap is stuck behind.
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Why Product Leaders choose Kumo
Here's exactly how Kumo changes the way your product team ships AI features.
Days
From feature idea to production
Define a prediction in plain query language, get a production API endpoint. No 6-month ML engineering queue. Ship AI-powered features on your product timeline.
Explainable
Predictions your PMs understand
Every prediction comes with feature importance and explainability. Your product team can understand why a user got a score — not just what the score is.
$100M+
Revenue from product predictions
DoorDash embeds Kumo predictions directly into their product — restaurant recommendations, notification timing, reranking — driving hundreds of millions in GMV.
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 — Wait for ML engineering: Every AI-powered feature requires ML engineering to build custom models, taking months. Your product roadmap stalls while the ML team works through their backlog. Features your users need today ship next year.
Path 2 — Try LLMs: LLMs generate text, not the structured predictions your product needs — churn scores, recommendations, propensity. They can't reason over your relational data to deliver the precise, real-time predictions that power great product experiences.
Kumo delivers production-grade prediction APIs your product team can integrate immediately. Define what you want to predict, connect your data warehouse, and get an endpoint your engineers can ship — in days, not quarters.
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
