Start-up raises funds for AI-powered data stack automation

Start-up raises funds for AI-powered data stack automation

Business news |
By Jean-Pierre Joosting

Numbers Station, the intelligent data stack automation platform, has announced a $12.5 million series A funding round led by Madrona, with participation from Norwest Venture Partners, Factory, and notable angel investors, such as Cloudera Co-founder Jeff Hammerbacher. The funding follows a recent $5 million seed round, bringing total capital raised to $17.5 million. Using foundation model technology pioneered at the Stanford AI lab, Numbers Station enables all data workers to rapidly generate insights by automating tedious data work, starting with data transformation work.

“Foundation models have proven incredibly powerful at democratizing access to AI, especially in language, image, and code generation. At Numbers Station, we pioneered new technology that brings the power of foundation models to the modern data stack, empowering data analytics teams to automate tedious data work and get the data they need when they need it. Ultimately, Numbers Station enables any data or domain expert to rapidly get insights from their messy, siloed, and most complex data via natural language interactions with intelligent foundation models — no code or AI expertise needed. This is just the start of self-service data stack automation to help organizations generate insights at the speed of business,” said Number Station Co-founder and CEO Chris Aberger, Ph.D.

From the most basic data tasks, such as formatting, to the most complex, such as entity resolution or extraction, Numbers Station enables all information workers to leverage AI to transform data. With Numbers Station, data workers can easily connect to their data warehouse, use a conversational interface to prototype intelligent data transformations powered by proprietary foundation model technology, and then deploy their pipelines back into their data warehouse. Customers can apply these new automated pipelines to business use cases spanning any department managing heavy analytics.

By empowering anyone to securely automate data-intensive workflows on the modern data stack, Numbers Station frees data engineers from endless backlogs of requests and eliminates costly communication gaps between data teams. Early customers are applying Numbers Station across departments — from managing user and customer data for marketing and customer success to building transparency in product and finance analytics. With the power of the proprietary foundation model technology developed by the team, enterprises can get these insights quickly — in hours, not months — and rapidly share them with decision-makers.

“Foundation models and related technologies will have significant impact on enterprises. Despite continuing to consume an inordinate amount of time for enterprise analysts, data prep and transformation remains a largely unsolved problem that has been devilishly difficult to productize. We believe foundation models, specifically how the Numbers Station team is applying them, provide a truly ‘zero to one’ opportunity to address the massive challenge of dealing with messy data. Further, data prep is just the first step in the team’s ambitious vision. For instance, immediately connecting these intelligent transforms into automated workflows is another key advantage of the Numbers Station approach,” said Madrona Managing Director Tim Porter. As part of the financing, Tim joined the Numbers Station board.

Based on the research developed at the Stanford AI Lab by Chief Scientist and Co-founder Ines Chami, along with founding team Chris Aberger, Sen Wu, and Chris Re, the Numbers Station self-service platform works across any foundation model in conjunction with proprietary foundation models and algorithms developed to manage data-intensive tasks. The Numbers Station platform offers a solution that lowers the technical barrier to setting up data pipelines, cleaning data, and performing various data-related tasks, including merging, cleaning, enriching, and summarizing messy organizational data.

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