By using this website, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Cookie Policy for more information.
Request demo
Power your products with predictions in days regardless of your ML experience
We automate all major steps in the ML lifecycle from raw data ingestion to sustained production deployment
Query the future in our ‘Predictive Querying’ language that is as easy to use as SQL
Leverage automated machine learning with state of the art Graph Neural Network technology to drive higher accuracy even with less data
Deliver more predictions more quickly across every team, enabling your entire enterprise to more proactively choose the future you want
One platform
Customer Acquisition
Customer Retention and Next Best Action
Personalization
Forecasting and Anomaly Detection
Entity Resolution and Knowledge Graph Enrichment
Fraud and Abuse Detection
Anti Money Laundering
Embeddings for Data Scientists
Read about our platform capabilities in more detail
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
Kumo.ai enables users across the enterprise to rapidly develop, evaluate and deploy state-of-the-art predictions in production in hours instead of months.
When marketing resources are constrained, it is critical for businesses to identify and focus on the future high value customers that will have the biggest impact as these users represent the biggest opportunity for the business.
If you’ve ever unsubscribed from a notification, you’re aware of what a poor notification experience is. Similarly, if you’ve ever clicked on a notification, you’ve likely found it to be useful and relevant. In this article, we’ll share what a good notifications strategy looks like, and how to use AI to improve your approach for every user.
Personalization is all around us. If you’ve ever received a relevant recommendation on a website, a notification from an app, or a promotion in your inbox, you’ve been delivered a personalized experience.
Customers are watching their pennies now, just like the rest of us. This means at a time that companies are doing everything they can to grow revenue, they need to be on high alert to anything that could push their customers away.
Predictive analytics traditionally refers to the process of identifying meaningful patterns in historical data in order to predict future trends and events. Having the ability to forecast potential scenarios can help drive strategic decisions.
As every data scientist knows, it takes a significant number of manual steps to go from a business problem with raw data to a fully operational production model.
Most recommendations, promotions, and advertisements people encounter on a daily basis are the result of many complex data pipelines that transform consumer behaviors into targeted predictions.
Having a fully automated detection system at scale is critical for organizations to ensure trust with their customers, however this is incredibly difficult to do effectively in practice. In this blog post, we’ll dive into the mechanics of these systems and talk about some of the traditional approaches.
Unleash the predictive power of your enterprise data