Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Use case
Which leads will convert is a million-dollar question - and most companies answer it badly. Hire an ML team, and they spend months hand-crafting features from flat tables, capturing a fraction of the signal. Try LLMs, and they tokenize your CRM data without understanding the relationships between tables. KumoRFM is the world's first foundation model for relational data. It connects directly to your data warehouse, learns from the relationships across CRM, product usage, support, and billing tables, and delivers 85%+ AUROC - with zero feature engineering, in under an hour.
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Traditional ML teams spend months on feature engineering. LLMs treat your data as tokens and miss the relationships. Kumo is neither - it's a new category.
Works on raw relational data
Connects to your database - no ETL, no flat tables
Zero feature engineering
Graph transformer discovers signals across tables automatically
Continuously learning
Models retrain as your data changes
One platform, every prediction
Churn, LTV, fraud, recommendations - same architecture
Lower infrastructure cost
Replace per-use-case pipelines with a single platform
Stanford RelBench Benchmark
On Stanford's RelBench benchmark - across 30 prediction tasks on 11 relational databases - KumoRFM outperforms both LLM-based approaches and expert PhD data scientists with hand-crafted features. Before any fine-tuning.
LLM
GPT-4 + AutoML
PhD Data Scientist
Manual feature engineering
Kumo
Relational Foundation Model
Average AUROC across 30 prediction tasks on 11 relational databases. Source: RelBench by Stanford (NeurIPS 2024).
In practice
A B2B company connects their CRM, product usage, support, and billing tables to Kumo. Within hours, KumoRFM delivers lead scores that already outperform their existing model - with zero feature engineering. Then they fine-tune on their Kumo Enterprise instance, incorporating proprietary signals specific to their sales cycle, pushing accuracy even further - because every percentage point matters.
No separate pipelines. No months of iteration. No waiting.
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
How Kumo is different
You know how ChatGPT can answer questions about language without you training it first? KumoRFM works the same way -but for predictions on your business data. Connect your database, and ask questions like:
Kumo gives you accurate answers instantly -without feature engineering, without building a separate model for each question, and without a months-long ML project. Here's why the existing approaches fall short:
In practice: a retail company connects their customers, orders, products, and events tables to Kumo. Within hours, they get churn predictions, personalized recommendations, and demand forecasts -all from the same model, all more accurate than what their data science team built over six months. Then they fine-tune on their enterprise instance to push accuracy even further, because every percentage point matters. No separate models. No feature engineering. No waiting.
3.2x
More accurate
Than traditional ML on lead conversion
<1 hr
To production
From raw tables to deployed scores
85%+
AUROC
On real-world B2B lead conversion
0
Manual features
Discovered automatically from relational data
Trusted by innovative companies
Leading companies use Kumo to drive measurable business outcomes from their existing relational data.
Lead-scoring models built on Kumo's relational deep learning delivered 5.4x improvement in lead conversion.
Enhanced recommendation accuracy across 80M monthly predictions, driving measurable revenue growth.
Powering personalized product recommendations for one of the UK's largest and most established supermarket chains.
Personalized solutions recommendations driving expansion revenue growth through relational data intelligence.
New recommendation model delivering measurable uplift in ad targeting and clickthrough performance.
The problem
Hire an ML team: They spend months hand-crafting features from flat tables - company size, industry, last email open. This captures a fraction of the signal. It costs $50K to $1M+ and 6–12 weeks per model. Sales reps learn to ignore the scores within weeks. And 53–88% of these models never even reach production (Gartner, IDC).
Try LLMs: They tokenize your CRM data - treating rows and columns as text. They have no concept of primary keys, foreign keys, or the relationships between contacts, accounts, and opportunities. They miss the structural signal that actually predicts conversion.
The problem is not the algorithm. It is the data representation. Neither approach understands relational data.
The approach
KumoRFM connects directly to your data warehouse and operates on your relational database as-is. It learns from the relationships between contacts, accounts, opportunities, product usage events, and support interactions - no feature engineering required.
The Graph Transformer attends across multiple columns, multiple tables, and multiple hops - discovering signals that no feature engineer would think to build and no LLM could infer from tokenized text.
KumoRFM already beats human data scientists out of the box - without any training on your data. Fine-tune on Kumo Enterprise to push accuracy even further. The result: 85%+ AUROC on real-world lead conversion, with interpretable attribution for every score.
Setup takes less than an hour. No ETL. No feature store. No hiring.
01
Point Kumo at your CRM, product usage, marketing, support, and billing tables. Kumo infers the relational schema automatically - connecting tables via primary key and foreign key relationships.
02
Predictive Query Language reads like SQL but describes future outcomes. No ML expertise required.
PREDICT COUNT(orders.*, 0, 7) > 0
FOR EACH users.user_id
IN (0, 1, 2)“Which users will place an order in the next 7 days?”
03
Kumo trains a relational deep learning model, evaluates accuracy, and deploys to production. Scores update continuously. Every prediction includes human-readable explanations rooted in your actual data.
Backed by peer-reviewed research
Not incremental tuning. A fundamentally different data representation - validated across 40+ peer-reviewed papers at NeurIPS, ICML, and KDD by the team behind PyTorch Geometric.
The Graph Transformer attends across multiple tables and multiple hops simultaneously - learning from a contact's account history, their colleagues' product usage, support ticket patterns, and billing events. Fey, Hu, Huang & Leskovec showed this produces fundamentally more accurate predictions than any flat-table approach.
Read the paperTraditional scoring requires hand-crafting 50-100 features from joined tables - 80% of project time and $50K-$1M+ per model (McKinsey). The RDL paper proved that learning directly from relational structure eliminates this. Kumo discovers filters, correlations, and aggregations no engineer would think to build.
Read the paperKumoRFM is pre-trained on diverse relational databases - retail, finance, healthcare, SaaS. Like how GPT transfers language understanding to new tasks, KumoRFM transfers relational understanding to your data. Huang, Fey & Leskovec showed this enables accurate predictions even with limited historical data.
Read the paperStanford's RelBench benchmark tested predictions across 11 real-world relational databases and 30 tasks. Kumo achieved 81% average AUROC - compared to 75% from PhD data scientists with hand-crafted features and 63% from LLM-based approaches.
Read the paperWhat this means in practice: a contact who works at an account where three colleagues already use your product, who opened a pricing page after a support ticket was resolved, scores differently than a cold lead with the same job title. Traditional models cannot see this. Kumo can.
From connecting Snowflake tables to deployed, explainable lead scores - in a single session.
Proven at scale
The same platform powers predictions across acquisition, retention, personalization, fraud, and more.
Industry data from McKinsey, Gartner, and Dimension Research shows why most lead scoring projects fail.
Input signals
Traditional
50–100 hand-crafted features
With Kumo
Entire relational database
Setup time
Traditional
6–12 weeks of engineering
With Kumo
Under 1 hour
Cost per model
Traditional
$50K – $1M+ (McKinsey)
With Kumo
Fraction - single platform
AUROC
Traditional
0.65–0.75
With Kumo
0.85+ (RelBench validated)
Explainability
Traditional
Black box or basic SHAP
With Kumo
Human-readable relational explanations
Maintenance
Traditional
~30% annual cost (Dimension Research)
With Kumo
Continuous automatic retraining
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 paperShare this with your team
See what Kumo can predict from your existing relational database.