Combine graph learning with GenAI to build better ML models faster
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created in a single day
scale graphs used as input
versus in-house ML baselines
with multiple customers
Built by AI leaders from AirBnB, Google, LinkedIn, and Pinterest. Deployed and trusted by the world’s leading organizations.
for your mission critical use cases
Personalization
Customer Retention & Next Best Action
Customer Acquisition
Forecasting and Anomaly Detection
Entity Resolution and Knowledge Graph Enrichment
Fraud and Abuse Detection
Anti Money Laundering
Embeddings for Data Scientists
Leverage state of the art Graph Neural Networks to learn directly from your raw relational data without manual feature engineering, delivering dramatically higher accuracy
Simplify your infrastructure and optimize your costs by removing the need for feature pipelines, feature stores, etc.
Deliver ROI faster and across more use cases through our end to end platform covering all major steps in the ML lifecycle including data prep, model training, XAI, deployment, and ML Ops
With REST APIs backed by high availability SLAs, SOC 2 Type II and GDPR compliance, options for both SaaS and Private Cloud operating models.
Read about Kumo's capabilities in more detail
At Stone, we take pride in deeply understanding our small and medium-sized business customers and what they will do next. This allows us to provide them with products and services that provide the best value and thus retain them as customers for the long term.
A critical part of doing that is quickly implementing various highly accurate predictive models for many aspects of the customer journey, such as churn prediction, lifetime value, customer intent, and more.
Our data scientists loved Kumo's solution, which allows for a declarative way to specify modeling problems and for high iteration, excellent model performance, and a quick path to productization, thus increasing the productivity of these critical teams.
 
CTO at Stone Co.
At Whatnot, AI plays a critical role in personalizing the shopper experience, driving cross-sell across categories and predicting future aggregate shopper behavior so we can shape our broader marketplace.
To this end, we are working with Kumo to deliver a service that is truly ground-breaking, allowing us to not only quickly launch these needed predictions with their very simple predictive querying language and accompanying APIs, but also drive dramatic model quality gains, including a doubling of both precision and recall over existing baselines in initial experiments. We've been thrilled by the progress so far, and the ability of the Kumo product to allow even non-technical teams to harness the power of AI from our data in the future.
VP of Engineering at Whatnot
At Yieldmo, we are hyper focused on cutting edge AI approaches to maximize the value of advertising for buyers, sellers, and consumers in a privacy-first way.
Our recent collaboration with the Kumo team offers us the opportunity to leverage their innovative graph neural network technology within our next-generation machine learning (ML) models for ad inventory curation.
So far, the early results have been very promising, showing a significant improvement in predictive power compared to leading solutions in the market today.
Head of Analytics and Data Science
Discover how Kumo AI utilizes hybrid graph neural networks (GNNs) to revolutionize recommendation systems. Explore the effectiveness of this approach in both Kaggle data science challenges and real-world customer scenarios.
Kumo is a cutting-edge predictive AI solution
Join our webinar to learn more about Kumo in Snowflake
Predictive machine learning pierces through the complexity of massive datasets and serves up the power to predict—with precision—events and trends that shape our world.
Discover the power of AI personalization for your business. Enhance customer engagement and satisfaction with tailored experiences driven by artificial intelligence. Learn more about our solutions today!
Recently, we earned the prestigious recognition of Forbes Top 50 AI Startups. As we celebrate this milestone, we extend our heartfelt appreciation to our dedicated employees and their unwavering commitment to excellence.
Deploying GNNs poses significant challenges. See how Kumo’s architecture is designed to handle at scale deployments
Announcing distributed GNN training solution for PyG via torch_geometric.distributed
How to build an efficient and scalable end-to-end system for graph learning in data warehouses.
Last month, Jure Leskovec, Co-founder and Chief Scientist at Kumo, unveiled the revolutionary Kumo.AI platform, marking a significant milestone in reshaping the landscape of the machine learning lifecycle.
We’re excited to announce the general availability of the Kumo.AI platform, enabling the rapid creation and deployment of state-of-the-art AI models on private enterprise data. AI practitioners can now use our intuitive SQL-like Predictive Querying Language to build multiple task-specific AI models in a single day. The Kumo.AI platform empowers enterprises to unlock customer-focused use cases, such as personalization, churn and LTV prediction, fraud detection, and forecasting
Using AI and predictive machine learning (ML) to get actionable forward-looking insights from data are no longer a competitive edge, rather a necessity for ecommerce businesses. With so many options available, consumers expect high quality, personalized experiences that give them exactly what they are likely interested in.
Unleash the predictive power of your enterprise data
How Kumo achieved model performance 20% better than the baseline for predicting personalized recommendations
Recommendation systems are one of the most powerful tools that consumer marketplaces have available to them.
A leading US online personal finance & banking company wanted to improve revenue by doing targeted outreach to client’s users who might be interested in taking revenue-generating actions on their site – such as opening new accounts.
A Fortune 500 on-demand food delivery service wanted to increase its revenue by better personalizing their recommendations.
In an effort to help reduce customer churn and retain revenue, this payment company had built a retention team and created retention programs.
A leading grocery chain wanted to increase their sales by sending personalized physical flyers with a dozen coupons to their clients to encourage them to buy things they might want to buy.
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