Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Forecasting & Planning
Predict demand, inventory needs, workforce requirements, and budgets — all from your existing relational data. Kumo learns from the connections that traditional time-series models flatten away.
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6
Prediction types
Demand to budgets
0
Feature engineering
Fully automated
<1 hr
To production
Per use case
15+
Industries
Retail, supply chain, SaaS
Loved by data scientists, ML engineers & CXOs at
6 forecasting use cases
Each use case runs on the same relational foundation model. No per-model pipelines. No per-use-case feature engineering.
Predict demand at the SKU-store-week level by connecting promotions, seasonal patterns, pricing, and supplier constraints.
Optimize stock levels across locations by learning from demand patterns, lead times, and supply chain relationships.
Forecast staffing needs from historical demand, seasonal patterns, event schedules, and operational constraints.
Predict infrastructure and resource requirements by learning from usage trends and growth patterns.
Forecast seasonal demand shifts by learning from multi-year patterns, promotional calendars, and external event data.
Predict optimal budget distribution across channels, campaigns, and regions by learning from historical spend-to-outcome relationships.
See how relational learning compares to time-series and tabular approaches.
Input data
Traditional
Flat feature tables per SKU
With Kumo
Full relational graph across products, stores, suppliers
Forecast granularity
Traditional
Category or store level
With Kumo
SKU-store-week precision
Feature engineering
Traditional
Months of manual work
With Kumo
Zero — automated from relational data
Cold-start items
Traditional
No history = no forecast
With Kumo
Graph context from similar items
External signals
Traditional
Manual integration per signal
With Kumo
Learns from connected tables automatically
Setup time
Traditional
6–12 months
With Kumo
Under 1 hour
Simply connect your data, start asking predictions, and get results.Want more control? Fine-tune the model for your specific use case.
Integrates directly with your warehouse, no additional pipeline setup.
Ask questions in plain English and let Kumo do the modeling for you.
Get clear predictions and push them instantly into your workflows.
PREDICT COUNT(transactions.*, 0, 90, days) = 0
FOR EACH customers.customer_id
WHERE COUNT(transactions.*, -60, 0, days) > 03 lines. No feature engineering. No pipeline code.
For developers
Kumo's Predictive Query Language (PQL) replaces months of feature engineering, model training, and pipeline work with a few lines of SQL-like syntax. Describe what you want to predict — Kumo handles the rest.
Get accurate predictions on relational data instantly—no training or ML setup required.
Read the KumoRFM announcement
Vanja Josifovski
CEO and Co-Founder
Former CTO at Airbnb and Pinterest
Jure Leskovec
Co-Founder & Chief Scientist
Stanford Professor · Co-creator of RDL and GNN
Hema Raghavan
Co-Founder & Head of Engineering
Former Sr. Director of Engineering at LinkedIn
Loved by data scientists, ML engineers & CXOs at
Peer-reviewed
Kumo is built on 40+ peer-reviewed papers at NeurIPS, ICML, and KDD.
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.
Read paperM. Fey, W. Hu, K. Huang, J. Leskovec et al.
Learning predictive models directly on relational databases, eliminating the feature engineering pipeline.
Read paperJ. Robinson, R. Miao, K. Huang et al.
An open benchmark for evaluating relational prediction methods across 11 databases and 30 tasks.
Read paperSee what Kumo can predict from your existing database — no feature engineering required.
