Foundational Secures $8M in Funding, Debuts its Data Development Platform

Foundational, a solution using advanced analysis to identify and prevent data issues in data platforms, is debuting its platform along with announcing $8 million in seed funding.

The round was led by Viola Ventures and Gradient, Google's AI-focused venture fund, with participation from Asymmetric Venture Partners and executives from Datadog, Intuit, Meta, Wiz, and others.

Foundational is forging partnerships with leading data organizations worldwide with the aim of redefining how data platforms are built and maintained at scale, according to the company.

With this funding, Foundational will continue to develop its technology and introduce new and exciting product capabilities that benefit data developers as they build at scale.

Foundational, powered by a proprietary AI-powered code analysis engine that can be instantly deployed through git, empowers data and analytics teams to confidently push continuous code changes, safeguarding against frequent issues and harmful incidents that affect critical data, according to the company.

The company aims to bridge the quality gaps in data product development at scale and simplify the data development process, enabling developers to make necessary code changes without the fear of causing unplanned data incidents.

“It has become a remarkably difficult task for developers to confirm crucial questions like, ‘What dashboards will this schema change affect?,’ ‘Can I deploy this code change safely?’ or ‘What data pipelines do we have that are not working?’” said Alon Nafta, Foundational’s CEO and cofounder. “The inability to validate simple code changes, and the lack of visibility and controls, and the time-consuming nature of data engineering create a dramatic bottleneck on innovation and adoption of data and AI. We’re excited to join forces with Viola Ventures and Gradient Ventures to build new tools and technologies as more companies try to leverage data at scale and introduce AI to the business.”

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