Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Next Best Action & Cross-sell
The right message, to the right person, through the right channel, at the right time. Kumo predicts every dimension of customer outreach from your relational data — no A/B test required.
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
Cross-sell to journeys
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 next-best-action use cases
From cross-sell targeting to omnichannel journey orchestration — one relational foundation model predicts every next step.
Predict which products or tiers a customer will upgrade to based on usage patterns and peer behavior.
Predict which campaigns, messages, and creatives will resonate with each audience segment.
Determine the optimal outreach channel - email, push, in-app, SMS - for each customer and context.
Predict the best time to reach each customer based on historical response patterns and activity cycles.
Rank accounts and opportunities by likelihood to close, helping reps focus on the highest-value actions.
Identify which promotions will re-engage each user, which products to feature, and what discount depth maximizes ROI.
Personalize store vs. delivery vs. pickup pathways by learning from cross-channel behavior and preference signals.
See how graph-learning AI compares to traditional campaign targeting and segmentation.
Data signals
Traditional
Campaign history only
With Kumo
Full relational customer graph
Personalization depth
Traditional
Segment-level
With Kumo
Individual-level
Channel optimization
Traditional
A/B testing weeks
With Kumo
Predicted per customer
Feature engineering
Traditional
Per campaign
With Kumo
Zero
Cross-sell accuracy
Traditional
Product rules
With Kumo
Learned from behavior graph
Setup time
Traditional
3-6 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.
