Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Industry
In the streaming wars, every percentage point of subscriber retention is worth millions. Traditional ML makes it worse by flattening relational data into feature tables, destroying the signals that actually predict subscriber churn, engagement, and content affinity. KumoRFM learns directly from the relationships in your data and is pre-trained on tens of thousands of datasets, delivering higher accuracy than any internally-built model, in hours, not months.
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Why media companies choose Kumo
Here's how Kumo transforms media and entertainment with relational AI.
$100M+
Revenue impact proven at scale
At DoorDash, Kumo-powered recommendations drive hundreds of millions in GMV. The same relational learning approach powers content recommendations, notification targeting, and subscriber retention in media.
10-50%
More accurate content recommendations
KumoRFM sees the full graph: user, content, genre, creator, viewing history, social signals, and time. Collaborative filtering only sees user-item pairs, missing the relational context that drives discovery.
Hours
New models without new pipelines
Content recs, churn prediction, engagement scoring, ad targeting, acquisition optimization. One platform replaces the custom pipeline you'd otherwise build for each use case.
Loved by data scientists, ML engineers & CXOs at
One platform, every prediction
From content recommendations to creator monetization, KumoRFM powers every media prediction from the same connected data. no per-model pipelines, no feature engineering.
Surface the right content for every viewer by learning from the full graph of viewing history, preferences, social signals, and content metadata — not simplified watch-next rules.
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Identify at-risk subscribers weeks before they cancel by detecting shifts in engagement patterns, content affinity decay, and social network disengagement across your relational data.
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Predict which content will drive the deepest engagement for each user by modeling the relationships between viewers, creators, genres, and interaction patterns in real time.
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Deliver more relevant ads by connecting viewer behavior, content context, and advertiser objectives in a single graph — driving higher CPMs without degrading user experience.
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Make data-driven licensing and acquisition decisions by predicting how new content will perform across different audience segments based on relational viewing patterns.
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Send the right notification at the right time by learning from each user's response patterns, content preferences, and engagement rhythms across channels and devices.
Improve search relevance by incorporating relational signals — what similar users watched, trending content in adjacent genres, and contextual viewing patterns — beyond keyword matching.
Build richer audience segments by learning from the full relational structure of viewer behavior, content affinity, and social connections — not just demographic buckets.
Predict which creators and content formats will drive the highest monetization by modeling the relationships between creator output, audience engagement, and revenue signals.
The media data advantage
Even with a world-class data science team, feature engineering fundamentally caps your accuracy. The moment you flatten relational tables into feature vectors, you discard the nuanced relationships between viewers, content, sessions, social connections, devices, and subscriptions. This isn't a team quality problem — it's a structural limitation of traditional ML that no amount of hiring solves.
KumoRFM is pre-trained on thousands of relational schemas. It has already learned what engagement, churn, and content affinity patterns look like across hundreds of data structures — the same advantage GPT has over custom NLP. Your team cannot replicate this breadth no matter how much time you give them. Foundation model scale changes the game.
KumoRFM doesn't replace your data science team — it 10x's them. They go from shipping 3–5 models per year to 50+ per quarter. Tedious feature engineering disappears; the interesting work — defining predictions, interpreting results, driving business impact — remains.
One platform powers content recommendations, churn prediction, engagement forecasting, ad targeting, and every other media prediction — from the same connected data.
95%
Less data preparation
Automated feature engineering
15–30%
Engagement lift
Over traditional recommendation models
20x
Faster to production
From months to hours
$2.4M
Average annual savings
Reduced ML infrastructure costs
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
40%
lift in prediction accuracy
“Beating internal XGBoost model on key metrics with far less data/features — on Kumo pre-trained. We replaced six months of pipeline work with a single afternoon.”
Matt Loskamp
GTM Data Science Leader, Enterprise Financial 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
