Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Industry
Subscriber churn costs the average operator $2B+ per year. Traditional ML makes it worse by flattening relational data into feature tables, destroying the signals that actually predict churn, network anomalies, and fraud. 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 telecom companies choose Kumo
Here's how Kumo transforms telecom operations with relational AI.
38%
Earlier churn detection
KumoRFM traces subscriber behavior across call records, plan changes, support interactions, network quality events, and social connections. It catches churn signals weeks earlier than usage-only models.
10-50%
More accurate network predictions
Pre-trained on thousands of relational datasets, KumoRFM understands network topology patterns, capacity constraints, and usage dynamics that your CDR-based models cannot capture alone.
Hours
Not quarters to production
Churn, network anomaly detection, plan recommendations, usage forecasting, LTV, and tower load prediction. Ship them all from one platform without custom ETL per model.
Loved by data scientists, ML engineers & CXOs at
One platform, every prediction
From subscriber churn to network anomaly detection, Kumo learns directly from the relational structure of your telecom data. no feature engineering, no per-model pipelines.
Detect network failures, congestion, and performance degradation in real time by learning from the relational structure of towers, cells, devices, and traffic flows.
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Recommend the right plan, add-on, or bundle for each subscriber by learning from usage behavior, device type, household composition, and similar customer segments.
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Predict data, voice, and messaging usage at the subscriber, tower, and regional level to optimize capacity allocation and reduce overprovisioning costs.
Score every subscriber by long-term revenue potential using relational signals from billing, usage trajectories, plan upgrades, and household account relationships.
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Forecast demand at each cell tower by learning from device density, usage patterns, event calendars, and population movement — enabling proactive infrastructure investment.
Catch SIM swap attacks, subscription fraud, and identity theft by learning from the relational signals between devices, accounts, payment methods, and behavioral anomalies.
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Determine the optimal retention offer — discount, plan change, loyalty reward — for each at-risk subscriber by learning from historical intervention outcomes across similar customers.
Predict service quality issues before they impact subscribers by learning from network topology, device performance, location data, and historical ticket patterns.
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Identify at-risk subscribers before they leave by learning from usage patterns, billing history, support interactions, and network quality signals across your entire customer graph.
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The telecom data advantage
Feature engineering destroys signal. Even with a world-class DS team, the feature engineering step is a structural bottleneck. Flattening relational tables into rows discards the nuanced relationships between subscribers → devices → towers → CDRs → billing → support tickets → network events. This isn't a team quality problem — it's a fundamental limitation of the approach. The signal lives in the connections, and flattening destroys them.
You only have your data. KumoRFM is pre-trained on thousands of relational schemas. It already knows what churn patterns, network anomalies, and fraud signals look like across hundreds of database structures. Your internal team, no matter how talented, can only learn from your data. KumoRFM brings the same advantage GPT has over a custom NLP model — breadth of pre-training that no single organization can replicate.
Your existing team will love it. KumoRFM 10x's your data science team — it doesn't replace them. Feature engineering disappears. Your team goes from shipping 3–5 models per year to 50+ per quarter. The interesting work — defining problems, interpreting results, driving business impact — stays with your people.
One platform powers churn prediction, network anomaly detection, fraud detection, capacity planning, and every other telecom prediction — from the same connected data.
95%
Less data preparation
Automated feature engineering
20–35%
Churn reduction
Over traditional rule-based systems
20x
Faster to production
From months to hours
$2.4M
Average annual savings
Per enterprise deployment
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
