Berlin Tech Meetup: The Future of Relational Foundation Models, Systems, and Real-World Applications
For Heads of Personalization
Segment-based personalization flattens customers into averages, losing individual relational context. KumoRFM predicts at the individual level by learning from each customer's full relationship graph. It's also pre-trained on thousands of datasets, capturing billions of patterns that give it accuracy no in-house model can match. And your team tests new personalization hypotheses in minutes, not the months-long cycle you're stuck in today.
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Why Personalization Leaders choose Kumo
Here's how Kumo transforms personalization from segment-based rules to true individual understanding.
1:1
True individual personalization
Move beyond segment-based rules. KumoRFM predicts at the individual level by understanding each customer's full relational context — purchases, interactions, preferences, and connections.
$100M+
Revenue from personalized experiences
DoorDash uses Kumo for restaurant recommendations, notification personalization, and send-time optimization — all from one model that understands user-restaurant relationships.
Minutes
To test a new personalization hypothesis
Define a new prediction in Kumo's query language and test it on your actual data in minutes. No 3-month A/B test planning cycle needed to validate an idea.
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 — Rule-based segmentation: Segments flatten individual nuance into broad cohorts. A “high-value female 25–34” segment treats millions of unique people identically, leading to generic experiences that underperform and erode engagement over time.
Path 2 — LLMs for personalization: Large language models can generate personalized content, but they can't predict individual behavior on your relational data. They have no understanding of purchase graphs, interaction histories, or the relationships between customers, products, and channels.
KumoRFM predicts individual actions by understanding relational patterns across your entire data warehouse — what each customer will buy, click, churn from, or engage with next. True 1:1 personalization, not smarter segments.
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
