Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Industry
Demand forecasting errors cost supply chain teams millions in excess inventory, stockouts, and expedited shipping. Traditional ML makes it worse by flattening relational data into feature tables, destroying the signals that actually predict demand patterns, supplier disruptions, and lead time variability. 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 supply chain leaders choose Kumo
Here's how Kumo transforms supply chain operations with relational AI.
42%
Better demand sensing accuracy
KumoRFM learns from the full supply chain graph: orders, suppliers, SKUs, warehouses, logistics routes, and seasonal patterns. It captures multi-hop demand signals that single-table forecasting models miss.
10-50%
More accurate risk predictions
Pre-trained on thousands of relational datasets, KumoRFM understands supplier risk patterns, lead time variability, and disruption cascades. It scores risk across your entire supplier network simultaneously.
Hours
To deploy across use cases
Demand sensing, supplier risk, inventory optimization, lead time prediction, disruption forecasting. One platform handles them all without building custom feature pipelines per supply chain node.
Loved by data scientists, ML engineers & CXOs at
Use cases
Every supply chain use case below runs on the same platform, the same connected data, with zero feature engineering.
Predict demand shifts weeks ahead by learning from the full relational structure of orders, products, customers, and external signals — not just lagged time series.
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Score supplier reliability by connecting delivery history, financial health, geographic exposure, and second-tier dependencies into a single predictive graph.
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Dynamically balance stock levels across warehouses and SKUs by learning demand patterns, lead times, and seasonal signals from your relational data.
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Forecast actual delivery timelines by modeling the relationships between suppliers, shipping routes, order complexity, and historical performance.
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Detect supply chain disruptions before they cascade by learning early-warning patterns across supplier networks, logistics data, and external risk signals.
Optimize pick, pack, and ship decisions by predicting order volumes, warehouse capacity constraints, and delivery SLA risks across your fulfillment network.
Improve delivery efficiency by learning from historical route performance, traffic patterns, weather data, and customer delivery preferences.
Identify savings opportunities by analyzing spend patterns, contract terms, supplier alternatives, and volume consolidation signals across your procurement data.
Predict incoming material defects by connecting supplier quality history, batch characteristics, inspection results, and production outcomes.
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The supply chain 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 drive predictions. Your supply chain data connects suppliers → purchase orders → shipments → warehouses → SKUs → demand signals → logistics partners. That structure encodes why demand shifts, which suppliers are at risk, and where bottlenecks will form. Flatten it, and you lose it. 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 demand patterns, supplier failure modes, and logistics signals look like across hundreds of different data structures. Your team — no matter how talented — can only learn from the data inside your four walls. KumoRFM brings external pattern knowledge that no in-house model can replicate.
Your existing team will love it. KumoRFM 10x's your data science team. Feature engineering — the work that consumes 80% of their time — disappears entirely. Teams go from 3–5 models per year to 50+ per quarter. Your best people stop writing ETL and start solving the interesting problems: defining prediction targets, interpreting results, and driving business decisions.
One platform. Same connected data. Demand sensing, supplier risk, inventory optimization, lead time prediction, and every other supply chain prediction — without a single feature pipeline.
95%
Less data preparation
Automated feature engineering
15–30%
Inventory optimization
Reduction in excess and stockouts
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
