Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Acquisition & Lead Scoring
Stop scoring leads on firmographics alone. Kumo learns from the full relational graph — CRM interactions, product usage, support tickets, and marketing touchpoints — to predict who will convert and why.
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
Leads to referrals
3.2x
Accuracy lift
vs. traditional scoring
<1 hr
To production
Per use case
0
Feature engineering
Fully automated
Loved by data scientists, ML engineers & CXOs at
7 acquisition use cases
Every acquisition prediction runs on the same relational foundation model. No per-model pipelines.
Score leads using the full relational graph - CRM, product usage, support, and marketing interactions - not just company size and job title.
Predict which accounts are most likely to convert by learning from firmographic data, engagement patterns, and cross-account relationships.
Find prospects that resemble your best customers using relational similarity - beyond demographic overlap.
Predict individual purchase probability using transaction history, browsing behavior, and product interaction patterns.
Attribute conversions across touchpoints by learning the relational paths that actually drive outcomes.
Identify which customers are most likely to refer others based on satisfaction signals, social connections, and engagement depth.
Predict which free trial users will convert to paid by learning from product usage, feature adoption, and onboarding patterns.
See how graph-learning AI compares to legacy lead scoring and attribution models.
Data inputs
Traditional
Firmographics only
With Kumo
Full relational graph
Signal quality
Traditional
Static, stale attributes
With Kumo
Dynamic cross-object signals
Feature engineering
Traditional
Months of manual work
With Kumo
Fully automated
Cross-object signals
Traditional
Not captured
With Kumo
Learned automatically from graph
Model freshness
Traditional
Retrained quarterly
With Kumo
Continuously updated
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.
