Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Industry
Every lost student costs $50K-$200K in tuition, and most retention models catch them too late because traditional ML flattens relational data into feature tables, destroying the signals that actually predict dropout risk, enrollment, and learning paths. 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 education leaders choose Kumo
Here's how Kumo transforms education with relational AI.
40%
Earlier at-risk student detection
KumoRFM learns from the full student graph: courses, grades, attendance, advisor interactions, financial aid, and extracurricular engagement. It catches dropout signals months earlier than GPA-based models.
10-50%
More accurate outcome predictions
Pre-trained on thousands of relational datasets, KumoRFM understands academic patterns across student demographics, course sequences, and institutional contexts. Better predictions mean better interventions.
Hours
To deploy predictive models
Student retention, learning path personalization, enrollment prediction, course recommendations, and outcome forecasting. Ship them all from one platform without building separate analytics pipelines.
Loved by data scientists, ML engineers & CXOs at
Use cases
From student retention to learning personalization, Kumo learns from every relationship in your data to deliver predictions that traditional ML misses.
Predict which students are at risk of dropping out by learning from enrollment patterns, academic performance, engagement signals, and peer network effects across your institution.
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Personalize learning paths by analyzing student performance histories, course prerequisites, peer outcomes, and learning style signals connected across your academic data.
Forecast enrollment numbers by learning from application patterns, demographic trends, financial aid data, and historical yield rates across interconnected admissions data.
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Recommend optimal courses by connecting student interests, academic history, career goals, peer success patterns, and faculty expertise in a relational graph.
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Forecast student outcomes — graduation rates, career placement, academic achievement — by learning from the full web of academic, social, and institutional signals.
Score student engagement levels by analyzing attendance patterns, LMS activity, assignment submissions, peer interactions, and extracurricular participation signals.
Optimize advisor caseloads by predicting student needs, risk levels, and intervention timing — ensuring high-risk students receive proactive support when it matters most.
Optimize financial aid allocation by learning from student need patterns, retention impact, academic performance, and institutional yield goals across your financial data.
Detect at-risk students early by connecting academic performance drops, engagement declines, financial stress signals, and social isolation patterns across institutional data.
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The education data advantage
Feature engineering destroys signal. Even with a great data science team, flattening relational tables into feature vectors discards the nuanced relationships that actually predict outcomes. Your education data connects students → courses → instructors → assignments → grades → attendance → financial aid. When you flatten that into a single row, you lose the structural signals that separate early intervention from too-late notification. This is a structural limitation of traditional ML, not a team quality problem.
You only have your data. KumoRFM is pre-trained on thousands of relational schemas across industries. It already knows what predictive patterns look like across hundreds of database structures — the same advantage GPT has over a custom NLP model. Your team, no matter how talented, cannot replicate this breadth of relational knowledge from a single institution's data.
Your existing team will love it. KumoRFM 10x's your data science team. Feature engineering disappears entirely. Teams go from 3–5 models per year to 50+ per quarter. The interesting work — defining retention strategies, interpreting student signals, driving interventions — remains. The drudgery vanishes.
One platform powers student retention, enrollment prediction, learning personalization, early warning systems, and every other education prediction — from the same connected data.
95%
Less data preparation
Automated feature engineering
15–25%
Retention improvement
Over traditional predictive 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
