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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
Effective customer outreach is challenging, and building an ML system to solve this isn’t exactly easy either.
ML-powered recommendations can be useful to your business in many ways. It can come in the form of a personalized homepage showing the user highly relevant content, or showing them a curated list of products they are most likely to be interested in.
One of the most important questions growth and GTM teams ask their data teams is - who are my most profitable customers and how can I get them to increase their spend?
Churn and retention models are useful to identify who is likely to stop using your product or service within a specified timeframe, say within the next week or month.
The explosive emergence of OpenAI’s ChatGPT has generated a wave of intense interest among enterprises of all sizes and industries in leveraging Large Language Models (LLMs) to create chat-based interfaces for their end users.
Using AI to enable efficient cross-selling and upselling has become increasingly important in helping businesses increase revenue and profitability while simultaneously improving customer engagement and loyalty.
Graph neural networks (GNNs) have emerged as a leading solution for machine learning (ML) applications, as many real-world problems and data can be effectively modeled as graphs.
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.
How to go from raw data to actionable insights from churn and retention analytics? Read more in the following blog post - Predicting Churn and Retention with Kumo.
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