Foundational, a startup aiming to bring order to the chaos of modern data infrastructure, announced today that it has raised $8 million in seed funding led by Viola Ventures and Gradient Ventures, Google’s AI-focused investment fund, with participation from angel investors and other venture firms. The company’s platform automatically maps and analyzes data teams’ code to identify potential issues, suggest fixes, and help prepare data for AI applications.
After operating in stealth mode for the past year and a half, Foundational is now making its technology generally available, with public companies like Ramp and Lemonade already signed on as customers. Foundational CEO and cofounder Alon Nafta told VentureBeat in an exclusive interview that the company decided it was the right time to share its story more broadly.
“In the past year, year and a half or so, we really built the ability to automatically map and understand the code that data teams are writing, and also connect it to this new AI world or universe that we have,” said Nafta. “And to really understand now how we can leverage it both with AI tooling, but also to prepare the data for AI to consume.”
Nafta, who previously worked in cybersecurity and data infrastructure roles, cofounded Foundational to address the growing pains many organizations face as they scale up their data capabilities. While tools like Snowflake, Databricks and dbt have made data more accessible than ever, they have also created sprawling, complex data pipelines that can be difficult to maintain.
“If you think about data in a normal organization, it moves around a lot of hands,” Nafta explained. “You have engineers that ingest it from somewhere, and then data engineers that need to clean it up. And then analysts or analytics engineers that do some modeling, and so on. You have a lot of exchanges.”
As a result, Nafta said, data teams often “lose touch of how all of those dependencies work with each other,” leading to confusion, quality issues and broken dashboards when changes are made. A survey by Gartner found that the average financial impact of poor data quality on organizations is $12.8 million per year. Across the 40 companies represented in the survey, the total cost of poor quality data added up to more than $510 million per year.
Foundational aims to solve this problem by automatically analyzing data teams’ source code to map data lineage and identify potential issues before they are deployed. The platform integrates with tools like GitHub to provide actionable suggestions and fixes directly within developers’ existing workflows.
“They will see our insights or warnings or suggestions directly in the interface that they already have,” said Nafta. Importantly, Foundational does not require access to the underlying data itself, only the metadata expressed in the code, reducing data privacy and security concerns.
Under the hood, the Foundational platform combines static code analysis, dynamic runtime analysis, and AI-powered techniques to build a comprehensive map of an organization’s data pipelines. It can identify issues like circular references, inefficient queries that may spike cloud costs and fields that are no longer being used and could be pruned.
“Once we have this full map of your data ecosystem, there are all kinds of powerful automation we can apply on top,” said Nafta. “We can warn you if a change is going to break downstream dependencies or suggest ways to optimize performance and reduce cost. We can even auto-generate documentation and data catalogs based on the code.”
Industry analysts say tools for maintaining data quality and consistency have become a critical need as companies seek to become more data-driven and adopt AI. A recent report by Gartner predicted that through 2024, 50% of organizations will adopt modern data quality solutions to better support their digital business initiatives.
But data quality is only part of the story. As companies race to implement machine learning models across their business, they are discovering that their data is often poorly suited for the task. Data scientists can spend up to 80% of their time on “data janitor work” like cleaning, labeling and structuring datasets before they can even begin building models.
Foundational sees an opportunity to help streamline this process through its code analysis approach. By understanding the full context and lineage of an organization’s data assets, the platform could potentially automate many data prep tasks and make recommendations on how to structure data for optimal model performance.
“The data piece is always relevant in two places, both how you use it to improve your AI initiatives and whatnot,” said Nafta. “But also how can you use AI to improve the data? It’s like this cycle. There’s a lot of technology and cool stuff here.”
With $8 million in fresh funding, Foundational plans to expand its engineering team and ramp up go-to-market efforts. The company currently has 16 employees based primarily in San Francisco and Israel. As more companies look to implement AI and machine learning, Nafta believes the Foundational platform will play a key role in helping them get their data house in order.
The seed round was led by Viola Ventures, an Israeli venture firm with over $3 billion in assets under management, and Gradient Ventures, Google’s new investment fund dedicated to nurturing the next wave of AI innovation. Other participating investors include Asymmetric Venture Partners and executives from Datadog, Intuit, Meta, Wiz, and others.
As data volumes continue to grow exponentially and AI becomes a mainstream business capability, Nafta believes the ability to automatically make sense of data pipelines and enforce quality will become table stakes. With its comprehensive code analysis approach, Foundational aims to become the foundational layer for a new era of data-driven innovation.
“We’re on a mission to give every organization high quality, trustworthy data they can build on top of,” said Nafta. “For us this is just the beginning.”
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Foundational, a startup aiming to bring order to the chaos of modern data infrastructure, announced today that it has raised $8 million in seed funding led by Viola Ventures and Gradient Ventures, Google’s AI-focused investment fund, with participation from angel investors and other venture firms. The company’s platform automatically maps and analyzes data teams’ code to identify potential issues, suggest fixes, and help prepare data for AI applications.
After operating in stealth mode for the past year and a half, Foundational is now making its technology generally available, with public companies like Ramp and Lemonade already signed on as customers. Foundational CEO and cofounder Alon Nafta told VentureBeat in an exclusive interview that the company decided it was the right time to share its story more broadly.
“In the past year, year and a half or so, we really built the ability to automatically map and understand the code that data teams are writing, and also connect it to this new AI world or universe that we have,” said Nafta. “And to really understand now how we can leverage it both with AI tooling, but also to prepare the data for AI to consume.”
Addressing the data quality crisis
Nafta, who previously worked in cybersecurity and data infrastructure roles, cofounded Foundational to address the growing pains many organizations face as they scale up their data capabilities. While tools like Snowflake, Databricks and dbt have made data more accessible than ever, they have also created sprawling, complex data pipelines that can be difficult to maintain.
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“If you think about data in a normal organization, it moves around a lot of hands,” Nafta explained. “You have engineers that ingest it from somewhere, and then data engineers that need to clean it up. And then analysts or analytics engineers that do some modeling, and so on. You have a lot of exchanges.”
As a result, Nafta said, data teams often “lose touch of how all of those dependencies work with each other,” leading to confusion, quality issues and broken dashboards when changes are made. A survey by Gartner found that the average financial impact of poor data quality on organizations is $12.8 million per year. Across the 40 companies represented in the survey, the total cost of poor quality data added up to more than $510 million per year.
Automating data governance through code analysis
Foundational aims to solve this problem by automatically analyzing data teams’ source code to map data lineage and identify potential issues before they are deployed. The platform integrates with tools like GitHub to provide actionable suggestions and fixes directly within developers’ existing workflows.
“They will see our insights or warnings or suggestions directly in the interface that they already have,” said Nafta. Importantly, Foundational does not require access to the underlying data itself, only the metadata expressed in the code, reducing data privacy and security concerns.
Under the hood, the Foundational platform combines static code analysis, dynamic runtime analysis, and AI-powered techniques to build a comprehensive map of an organization’s data pipelines. It can identify issues like circular references, inefficient queries that may spike cloud costs and fields that are no longer being used and could be pruned.
“Once we have this full map of your data ecosystem, there are all kinds of powerful automation we can apply on top,” said Nafta. “We can warn you if a change is going to break downstream dependencies or suggest ways to optimize performance and reduce cost. We can even auto-generate documentation and data catalogs based on the code.”
Preparing data for an AI-powered future
Industry analysts say tools for maintaining data quality and consistency have become a critical need as companies seek to become more data-driven and adopt AI. A recent report by Gartner predicted that through 2024, 50% of organizations will adopt modern data quality solutions to better support their digital business initiatives.
But data quality is only part of the story. As companies race to implement machine learning models across their business, they are discovering that their data is often poorly suited for the task. Data scientists can spend up to 80% of their time on “data janitor work” like cleaning, labeling and structuring datasets before they can even begin building models.
Foundational sees an opportunity to help streamline this process through its code analysis approach. By understanding the full context and lineage of an organization’s data assets, the platform could potentially automate many data prep tasks and make recommendations on how to structure data for optimal model performance.
“The data piece is always relevant in two places, both how you use it to improve your AI initiatives and whatnot,” said Nafta. “But also how can you use AI to improve the data? It’s like this cycle. There’s a lot of technology and cool stuff here.”
Scaling up and looking ahead
With $8 million in fresh funding, Foundational plans to expand its engineering team and ramp up go-to-market efforts. The company currently has 16 employees based primarily in San Francisco and Israel. As more companies look to implement AI and machine learning, Nafta believes the Foundational platform will play a key role in helping them get their data house in order.
The seed round was led by Viola Ventures, an Israeli venture firm with over $3 billion in assets under management, and Gradient Ventures, Google’s new investment fund dedicated to nurturing the next wave of AI innovation. Other participating investors include Asymmetric Venture Partners and executives from Datadog, Intuit, Meta, Wiz, and others.
As data volumes continue to grow exponentially and AI becomes a mainstream business capability, Nafta believes the ability to automatically make sense of data pipelines and enforce quality will become table stakes. With its comprehensive code analysis approach, Foundational aims to become the foundational layer for a new era of data-driven innovation.
“We’re on a mission to give every organization high quality, trustworthy data they can build on top of,” said Nafta. “For us this is just the beginning.”
Author: Michael Nuñez
Source: Venturebeat
Reviewed By: Editorial Team
