Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Industry
NDR targets depend on predicting expansion and churn. Traditional ML makes it worse by flattening relational data into feature tables, destroying the signals that actually predict expansion revenue, churn risk, and adoption patterns. 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 B2B SaaS companies choose Kumo
Here's how Kumo transforms B2B SaaS growth with relational AI.
5.4x
More accurate lead scoring
KumoRFM learns from the full account graph: product usage, CRM interactions, support tickets, billing events, and team behavior patterns. It scores leads based on relational signals, not just firmographics.
3x
Better expansion revenue prediction
Pre-trained on thousands of relational schemas, KumoRFM understands PLG patterns, feature adoption curves, and account health signals. It predicts which accounts will expand before your CSM team notices.
Days
From hypothesis to production
Lead scoring, churn risk, expansion propensity, feature adoption, account health, and usage forecasting. Deploy all from one platform without separate data science sprints per model.
Loved by data scientists, ML engineers & CXOs at
One platform, every prediction
From PLG scoring to expansion revenue prediction, Kumo learns directly from the relational structure of your SaaS data. no feature engineering, no per-model pipelines.
Identify which free or trial users are most likely to convert by learning from product usage patterns, feature adoption sequences, team invites, and engagement signals across your entire user graph.
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Predict which accounts are ready to upgrade, add seats, or adopt new products by learning from usage trajectories, support interactions, and behavioral patterns of similar accounts that expanded.
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Detect accounts at risk of churning weeks before renewal by learning from declining usage, support ticket patterns, feature abandonment, and relational signals across users within each account.
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Predict which features each account will adopt next by learning from usage sequences, role-based behavior patterns, and adoption paths of similar customer segments.
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Generate a unified health score for every account by learning from product usage, support interactions, billing patterns, NPS signals, and stakeholder engagement across your entire customer base.
Predict which trial users will convert to paid by learning from activation milestones, feature exploration depth, team collaboration patterns, and integration setup behavior.
Forecast consumption patterns and optimize pricing tiers by learning from usage trajectories, account growth signals, and the relational structure between users, workspaces, and billing events.
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Predict which onboarding paths lead to fastest time-to-value by learning from setup sequences, feature activation order, support interactions, and success outcomes across your customer base.
Predict which support tickets will escalate before they do by learning from ticket history, account health signals, product usage context, and resolution patterns across similar cases.
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The SaaS 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 accounts → users → features → sessions → tickets → contracts → billing. 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, expansion, and conversion patterns 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, expansion scoring, PLG conversion, feature adoption, and every other SaaS prediction — from the same connected data.
95%
Less data preparation
Automated feature engineering
10–50%
Accuracy improvement
Over traditional ML models
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
