Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Industry
Patient readmissions cost U.S. health systems $25B+ every year. Traditional ML makes it worse by flattening relational data into feature tables, destroying the signals that actually predict readmissions, clinical deterioration, and treatment response. 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 healthcare leaders choose Kumo
Here's how Kumo transforms healthcare operations with relational AI.
32%
Fewer unplanned readmissions
KumoRFM learns from patient journeys across diagnoses, procedures, medications, providers, and social determinants simultaneously. It catches readmission risk signals that single-table models structurally miss.
10-50%
More accurate clinical predictions
Pre-trained on thousands of relational schemas, KumoRFM brings pattern knowledge from across healthcare and adjacent industries, boosting accuracy beyond what any single institution's data can achieve.
Days
From idea to production model
Readmission risk, length of stay, clinical trial matching, resource allocation. Deploy all from one platform without HIPAA-compliant feature pipelines for each use case.
Loved by data scientists, ML engineers & CXOs at
One platform, every prediction
From readmission prediction to billing anomaly detection, Kumo learns from your connected clinical data to power every prediction your data science and clinical informatics teams need.
Predict 30-day readmission risk by learning from the full relational context of patient encounters, diagnoses, medications, procedures, and social determinants of health.
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Detect patients at risk of rapid deterioration by modeling temporal relationships across vital signs, lab results, medication responses, and clinical notes in real time.
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Identify optimal treatment pathways by learning from outcomes across similar patient populations, comorbidity patterns, and treatment response histories in your EHR data.
Forecast bed demand, staffing needs, and equipment utilization by learning from admission patterns, length-of-stay trajectories, and seasonal trends across your facility network.
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Match eligible patients to clinical trials by learning from the relational patterns between patient profiles, trial criteria, diagnosis histories, and treatment responses.
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Predict appointment no-shows by modeling relationships between patient scheduling history, demographics, transportation access, and appointment characteristics.
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Optimize care transitions and discharge planning by learning from the relational patterns between patient acuity, post-acute options, and historical outcome trajectories.
Stratify patient populations by risk level by learning from the full web of clinical, behavioral, and social determinant signals across your connected health data.
Identify billing irregularities and coding errors by learning patterns across claims, procedures, provider behaviors, and payer rules — catching revenue leakage and compliance risks.
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The clinical data advantage
Feature engineering destroys signal. Even with a world-class data science team, the traditional ML approach forces you to flatten 10, 20, 50 relational tables into feature vectors. When you do that, you discard the nuanced relationships between patients, encounters, providers, medications, diagnoses, and lab results. A patient's deterioration trajectory spanning three departments and eight care transitions becomes a row of aggregated counts. This isn't a talent problem — it's a structural limitation of the approach itself.
You only have your data. Even the best internal model is trained on one health system's data. KumoRFM is pre-trained on thousands of relational schemas across industries. It has already learned what patterns look like across hundreds of different data structures — the same advantage GPT has over a custom NLP model. Your team, no matter how talented, cannot replicate foundation-model scale.
Your existing team will love it. KumoRFM doesn't replace your data scientists — it 10x's them. Instead of spending months on feature engineering and pipeline work, they define predictions in a simple query language. They go from shipping 3–5 models per year to 50+ per quarter. The tedious work disappears; the interesting work remains.
One platform powers readmission prediction, clinical deterioration alerts, treatment recommendations, resource optimization, and every other clinical prediction — from the same connected data, with the same team, at 10x the velocity.
95%
Less data preparation
Automated feature engineering
20–35%
Readmission reduction
Over traditional risk models
20x
Faster to production
From months to hours
$25B+
Annual readmission costs
The problem Kumo helps solve
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
