Berlin Tech Meetup: The Future of Relational Foundation Models, Systems, and Real-World Applications
For Chief Transformation Officers
AI transformation initiatives fail on two fronts: accuracy and speed. Traditional ML flattens relational data into feature tables, destroying the multi-hop relationships that encode the most valuable signals. KumoRFM learns from that relational structure directly for fundamentally better predictions. It's also pre-trained on thousands of proprietary and public relational datasets, bringing pattern knowledge no internal team has ever seen and delivering an additional 10-50% accuracy boost (proven on Stanford RelBench). And where each model takes quarters to build, KumoRFM ships production models in hours, letting your existing team deploy across every business unit immediately.
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Why Transformation Leaders choose Kumo
Here's how Kumo accelerates every phase of your AI transformation roadmap.
Weeks
To first production model
Traditional ML transformation takes 12-18 months per use case. Kumo delivers your first production model in days, not quarters.
10x
Team output multiplier
Same headcount, 10x more models. No new hires needed to scale AI capability across every business unit.
$50K–$1M
Saved per model
Each hand-built model costs $50K–$1M in engineering time. Kumo reduces the marginal cost of each new model to near zero.
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 transformation bottleneck
Path 1 — Build internal ML capability: It takes years and $10M+ to recruit, retain, and operationalize a full ML team. Each model costs $50K–$1M in engineering time, and most never reach production. Your transformation roadmap slows to a crawl.
Path 2 — Bet on LLMs: They hallucinate on structured business data. LLMs have no concept of primary keys, foreign keys, or the relational structure that encodes your most valuable predictive signals. They cannot reliably answer the questions your transformation depends on.
KumoRFM is the foundation of your AI transformation. It connects directly to your data warehouse, understands relational structure natively, and lets your existing team deploy accurate predictive models across every business unit — churn, LTV, fraud, recommendations — all from one platform, in weeks.
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
