Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Growth & Revenue
Know what every customer is worth, what they'll buy next, and what price will convert them. Kumo predicts the full growth equation from your relational data — LTV, pricing elasticity, basket composition, and expansion opportunities.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
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7
Prediction types
LTV to replenishment
0
Feature engineering
Fully automated
<1 hr
To production
Per use case
15+
Industries
Retail, SaaS, fintech
Loved by data scientists, ML engineers & CXOs at
7 growth use cases
From lifetime value to basket optimization — one relational foundation model powers every growth lever.
Predict individual LTV from transaction history, product affinity, support experience, and loyalty behavior.
Forecast revenue at the account, segment, or company level using relational signals across your business data.
Predict price elasticity per customer and product using transaction, competitive, and inventory context.
Identify high-potential markets and segments by learning from geographic, demographic, and behavioral patterns.
Predict which funnel steps, page layouts, and CTAs will convert for each visitor segment.
Predict complementary items that increase average order value using co-purchase patterns and session context.
Predict staple depletion and trigger timely reminders. Learn individual purchase cadences from transaction history.
See how graph-learning AI compares to traditional growth analytics and modeling approaches.
LTV prediction
Traditional
Cohort averages
With Kumo
Individual-level from relational data
Pricing optimization
Traditional
A/B tests at scale
With Kumo
Predicted elasticity per customer-product
Basket analysis
Traditional
Association rules
With Kumo
Learned from full purchase graph
Market expansion
Traditional
Manual market research
With Kumo
Predicted from geographic + behavioral patterns
Feature engineering
Traditional
Per model
With Kumo
Zero
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. 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.
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 relational database.
