The Trusted Data Foundation,
for Humans and Agents,
built for the AI Era.
Foundational creates a unified, deterministic knowledge graph, understanding the entire context of code and data movement across every repository, application, database, cloud, and on-prem system.
AI fails without a unified knowledge layer of code and data
AI fails at complex enterprise tasks.
AI doesn't do well when there are hundreds of repositories and enterprise applications. It needs a knowledge graph, and extremely capable CI/CD.
Data behaves differently than code.
AI primarily looks at code. Data is different, and metadata is typically not found in code.
Cross-tool lineage became critical.
Regulatory frameworks now require complete understanding of data transformations across multiple repositories and enterprise applications.
Success in AI requires it too.
Success in AI requires it too.
Code validation is the new bottleneck
AI is generating more code than ever, but for enterprise environments involving data, validation remains hard. AI reviewers don't understand data.
The problem
Traditional governance tools are just not built for this.
Catalogs are reactive and were built for the last mile in the data lake. They don't look at application code or programming languages. They're poorly adopted by the actual teams that do the work. The information is often manual and partial. The critical knowledge that exists in enterprise code is not captured.
Source code is critical context to AI
The business logic that defines what your data means lives in code, not query logs. Teams spend months and millions on manually understanding and tracking the code.
AI is struggling with cross-repo
Data and application dependencies between repositories and enterprise applications is very tough and expensive, even for frontier AI.
Legacy systems are too complex to track
Older code such as on-prem Java and COBOL contain years and years of complexity that is hard to analyze accurately.
Solve for cross-platform complexity. Gain visibility and provide context and controls to every agent, or human.
Foundational integrates directly with git, and leverages proprietary, determinstic source code analysis to extract the knowledge in every piece of code, and expose it to agents. Your software factory is finally here.
Visibility and Discovery
Easily understand where data originates, how it transforms, and what it impacts when code changes or knowledge is questioned.
Code and Data Graph
Lineage, governance, and metadata in a single graph, across every source and programming language such as Python, Java, and Go.
AI Security
Ensure agents are built on governed, fully-traceable data, and secure every dataset they access.
The Data Control Plane
that AI at scale requires.
“Foundational gave us instant clarity on our data. With column-level lineage, we stopped wasting hours chasing data lineage and started fixing issues before they became problems.”
Eyal El-Bahar, VP of BI and Analytics
"A data change can impact things your team may be unaware of, leading folks to draw potentially flawed conclusions about growth initiatives. We needed a tool to give us end-to-end visibility into every modification.”
Iñigo Hernandez, Engineering Manager
“With Foundational, our team has a secure automated code review and validation process that assures data quality. That’s priceless.”
Omer Biber, Head of Business Intelligence
“Foundational has been instrumental in helping us minimize redundancy and improve data visibility, enabling faster migrations and smoother collaboration across teams.”
Qun Wei, VP Data Analytics
“Foundational helps our teams release faster and with confidence. We see issues before they happen.”
Analytics Engineering Lead
Ready to get started?
Full visibility starts with the layers others cannot see.
Integrations
Every layer of your technology stack, covered.
Setup is 100% automated. Foundational gives you immediate time to value. No additional custom engineering or professional services needed. No TCO surprises, just 100% full visibility across every step in your data lifecycle.
