Kumo.AI, a Sequoia Capital-backed predictive AI company, announced the general availability of its Kumo.AI platform, enabling rapid creation and deployment of state-of-the-art AI models on private enterprise data. AI practitioners can now use Kumo.AI’s intuitive SQL-like Predictive Querying Language to build multiple task-specific AI models in a single day. Founded in 2021 by Stanford University Professor (Jure Leskovec) and former Airbnb and LinkedIn executives (Vanja Josifovski, Hema Raghavan), Kumo.AI helps enterprises unlock customer-focused use cases, such as personalization, churn and LTV prediction, fraud detection, and forecasting.
While generative AI excels at natural language understanding and generation, it lacks the capability to perform more complex, enterprise-specific tasks, such as predicting customer behavior or detecting fraudulent transactions. Kumo.AI is the first platform that bridges this gap by applying deep representation learning, a technology behind the current AI revolution, to enterprise data in data warehouses. The platform streamlines the most complex and time-consuming steps in the machine learning process by eliminating training data generation, feature engineering, feature pipelines, and feature stores.
“Our goal is to empower every data science team so they can address customer-focused use cases dramatically faster while providing superior accuracy,” said Co-founder and CEO, Vanja Josifovski. “Kumo.AI is redefining predictive AI by leveraging Graph Neural Networks, a technology pioneered by my Co-founder, Prof. Jure Leskovec at Stanford. We have built the most scalable and efficient graph learning platform, capable of generating predictions for hundreds of millions of entities, such as providing product recommendations for millions of users every day.”
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