Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Industry
Unplanned downtime costs $50K+ per hour. Traditional ML makes it worse by flattening relational data into feature tables, destroying the signals that actually predict equipment failures, quality defects, and yield. 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.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Why manufacturers choose Kumo
Here's how Kumo transforms manufacturing operations with relational AI.
47%
Fewer unplanned equipment failures
KumoRFM learns from sensor data, maintenance records, environmental conditions, production schedules, and failure cascade patterns across your entire asset network. It predicts failures before they propagate.
10-50%
Better quality predictions
Pre-trained on thousands of relational datasets, KumoRFM understands manufacturing process interactions, supplier quality correlations, and defect propagation patterns your process engineers cannot model manually.
Hours
Per model, not months
Predictive maintenance, quality control, yield optimization, supply chain visibility, and defect prediction. Ship them all from one platform without sensor-specific feature pipelines.
Loved by data scientists, ML engineers & CXOs at
Use cases
Every manufacturing use case below runs on the same platform, the same connected data, with zero feature engineering.
Forecast equipment failures before they happen by learning from sensor data, maintenance history, operating conditions, and component relationships across your entire asset fleet.
Explore →
Predict defects before they reach the end of the line by connecting raw material properties, process parameters, equipment state, and historical quality outcomes.
Explore →
Maximize production yield by learning the complex relationships between input materials, process settings, environmental conditions, and output quality.
Explore →
Optimize production schedules by predicting job durations, machine availability, material readiness, and order priorities across your manufacturing network.
Predict which equipment will fail and when by modeling the relationships between sensor readings, maintenance events, operational load, and component degradation patterns.
Gain end-to-end visibility into your supply chain by connecting supplier performance, inbound logistics, inventory levels, and production requirements in a single predictive graph.
Reduce energy costs by predicting consumption patterns across production lines, shifts, and equipment — identifying waste and optimizing scheduling for energy efficiency.
Explore →
Forecast warranty claims by connecting product configurations, manufacturing conditions, field performance data, and customer usage patterns to identify at-risk batches early.
Detect process anomalies in real time by learning normal operating patterns across interconnected sensors, machines, and production stages — catching deviations before they cause scrap.
Explore →
The manufacturing 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 manufacturing data connects machines → sensors → work orders → quality inspections → materials → operators → production lines. That structure encodes why equipment fails, which process parameters drive defects, and where yield losses originate. 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 failure modes, quality signals, and degradation patterns 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. Predictive maintenance, quality control, yield optimization, production scheduling, and every other manufacturing prediction — without a single feature pipeline.
95%
Less data preparation
Automated feature engineering
25–40%
Unplanned downtime reduction
Through predictive maintenance
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
