Foundational Raises $8 Million to Streamline Data Infrastructure
Foundational, a startup focused on improving modern data infrastructure, has announced the completion of an $8 million seed funding round led by Viola Ventures and Gradient Ventures, Google’s AI investment fund, along with participation from angel investors and additional venture firms. The company's platform automatically maps, analyzes, and enhances data teams' code to identify potential issues, suggest fixes, and prepare data for AI applications.
After operating in stealth mode for the past 18 months, Foundational is now ready to unveil its technology to the public. Notable companies like Ramp and Lemonade have already adopted their platform. In an exclusive interview, Foundational CEO and co-founder Alon Nafta emphasized the importance of sharing their story at this stage.
“In the past year and a half, we’ve developed the capability to automatically map and comprehend the code that data teams write, linking it to today's AI ecosystem,” said Nafta. “We aim to leverage this technology for AI tools and optimize data for consumption by AI.”
Addressing the Data Quality Crisis
Nafta, who has experience in cybersecurity and data infrastructure, co-founded Foundational to tackle the challenges organizations face as they scale their data operations. While tools like Snowflake, Databricks, and dbt have made data more accessible, they have also led to complex data pipelines that are difficult to maintain.
“In an organization, data often changes hands multiple times,” Nafta explained. “Engineers ingest it, data engineers clean it, and analytics engineers model it — creating numerous exchanges.”
As a result, data teams often lose sight of the interdependencies within their data systems, leading to confusion, quality issues, and broken dashboards. Gartner's survey indicates that poor data quality costs organizations an average of $12.8 million annually, with the total impact exceeding $510 million across 40 companies.
Automating Data Governance Through Code Analysis
Foundational seeks to address these challenges by automatically analyzing data teams' source code to map data lineage and identify issues before deployment. The platform integrates with tools such as GitHub, providing actionable insights and suggestions directly within developers' workflows.
“They will see our insights, warnings, or suggestions in the tools they already use,” Nafta noted. Foundational only requires access to metadata in the code, minimizing data privacy and security concerns.
The platform utilizes static code analysis, dynamic runtime analysis, and AI techniques to create a comprehensive map of an organization’s data pipelines. It can detect problems such as circular references, inefficient queries that increase cloud costs, and unused fields that can be removed.
“Once we have a complete map of your data ecosystem, we can implement powerful automations,” Nafta explained. “We can alert you to changes that might disrupt downstream dependencies, suggest performance optimizations, and even auto-generate documentation and data catalogs from the code.”
Preparing Data for an AI-Driven Future
As companies strive to become data-driven and embrace AI, the need for maintaining data quality and consistency has intensified. Gartner predicts that by 2024, 50% of organizations will adopt modern data quality solutions to support their digital initiatives.
However, data quality alone is not enough. As companies implement machine learning models, they often find that their data is inadequately prepared. Data scientists spend up to 80% of their time on data cleaning, labeling, and structuring before building models.
Foundational aims to streamline this process through its code analysis approach. By understanding data context and lineage, the platform can automate many data preparation tasks and offer recommendations on structuring data for optimal model performance.
“The data aspect is crucial for enhancing AI initiatives,” said Nafta. “But it’s also about using AI to improve data. It’s a continuous cycle with significant technological potential.”
Scaling Up and Future Prospects
With the new $8 million funding, Foundational plans to expand its engineering team and bolster its go-to-market strategy. The company currently employs 16 staff members, primarily based in San Francisco and Israel. As organizations increasingly adopt AI and machine learning, Nafta believes Foundational will be instrumental in helping them improve their data management.
The seed round was led by Viola Ventures and Gradient Ventures, with additional contributions from Asymmetric Venture Partners and executives from Datadog, Intuit, Meta, Wiz, and others.
As data volumes grow and AI becomes standard in business operations, the ability to effectively manage data pipelines and ensure quality will be essential. Through its comprehensive code analysis, Foundational seeks to establish itself as the foundational layer for a new era of data-driven innovation.
“We aim to empower every organization with high-quality, trustworthy data to build upon,” Nafta concluded. “This is just the beginning for us.”
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