Kumo aims to bring predictive AI to the enterprise with $18M in fresh capital

Kumo, a startup offering an AI-powered platform to tackle predictive problems in business, today announced that it raised $18 million in a Series B round led by Sequoia, with participation from A Capital, SV Angel and several angel investors. Co-founder and CEO Vanja Josifovski says the new funding will be put toward Kumo’s hiring efforts and R&D across the startup’s platform and services, which include data prep, data analytics and model management.

Kumo’s platform works specifically with graph neural networks, a class of AI system for processing data that can be represented as a series of graphs. Graphs in this context refer to mathematical constructs made up of vertices¬†(also called nodes) that are connected by¬†edges (or lines). Graphs can be used to model relations and processes in social, IT and even biological systems. For example, the link structure of a website can be represented by a graph where the vertices stand in for webpages and the edges represent links from one page to another.

Graph neural networks have powerful predictive capabilities. At Pinterest and LinkedIn, they’re used to recommend posts, people and more to hundreds of millions of active users. But as Josifovski notes, they’re computationally expensive to run — making them cost-prohibitive for most companies.

“Many enterprises today attempting to experiment with graph neural networks have been unable to scale beyond training data sets that fit in a single accelerator (memory in a single GPU), dramatically limiting their ability to take advantage of these emerging algorithmic approaches,” he told TechCrunch in an email interview. “Through fundamental infrastructural and algorithmic advancements, we have been able to scale to datasets in the many terabytes, allowing graph neural networks to be applied to customers with larger and more complicated enterprise graphs, such as social networks and multi-sided marketplaces.”

Using Kumo, customers can connect data sources to create a graph neural network that can then be queried in structured query language (SQL). Under the hood, the platform automatically trains the neural network system, evaluating it for accuracy and readying it for deployment to production.

Josifovski says that Kumo can be used for applications like new customer acquisition, customer loyalty and retention, personalization and next best action, abuse detection and financial crime detection. Previously the CTO of Pinterest and Airbnb Homes, Josifovski worked with Kumo’s other co-founders, former Pinterest chief scientist Jure Leskovec and Hema Raghavan, to develop the graph technology through Stanford and Dortmund University research labs.

“Companies spend millions of dollars storing terabytes of data but are able to effectively leverage only a fraction of it to generate the predictions they need to power forward-looking business decisions. The reason for this is major data science capacity gaps as well as the massive time and effort required to get predictions successfully into production,” Josifovski said. “We enable companies to move to a paradigm in which predictive analytics goes from being a scarce resource used sparingly into one in which it is as easy as writing a SQL query, thus enabling predictions to basically become ubiquitous — far more broadly adapted in use cases across the enterprise in a much shorter timeframe.”

Kumo remains in the pilot stage, but Josifovski says that it has “more than a dozen” early adopters in the enterprise. To date, the startup has raised $37 million in capital.