Foundational is a data governance platform that analyzes source code, pipelines, configurations, and BI metadata to provide end to end visibility, preventative controls, and real time understanding of how data flows across an enterprise environment. The platform is designed for data engineering teams, analytics engineers, data platform teams, and data governance leaders who work across complex, multi technology ecosystems that include both SQL and non SQL systems. The platform introduces an approach to governance that operates at the level of code rather than relying on manual processes or downstream detection. By parsing source code and metadata across warehouses, orchestration tools, transformation frameworks, and BI systems, Foundational produces complete lineage, build time impact analysis, and automated data contracts that help teams understand dependencies, assess change risk, and maintain a consistent standard for quality and reliability. This foundation supports AI governance by providing visibility into the inputs that feed machine learning features, models, and AI products so that teams can evaluate data quality, lineage, and policy adherence before training or deployment. Foundational helps users support several core data governance and data quality use cases. These include identifying the upstream source of a metric, predicting the effect of a schema or pipeline update, enforcing rules for critical datasets, and understanding how changes in one part of the stack may affect downstream analytics, feature pipelines, or AI systems. The platform supports organizations that need reliable data for reporting and machine learning, as well as teams that want to minimize data incidents and improve development speed across the data lifecycle. Key product capabilities include: • Automated end to end lineage across SQL and non SQL systems that updates when code changes • Build time impact analysis that evaluates downstream effects before code is merged • Automated data contracts that define and enforce expectations for structure, freshness, and behavior • Governance automation that centralizes change tracking, policy checks, and review workflows • Quality monitoring that prioritizes issues based on business impact by using lineage context • AI governance support that traces model inputs back to source systems, evaluates data quality for training data, and maintains visibility into the dependencies that influence AI output reliability Organizations adopt Foundational to replace manual tracing, fragmented governance tools, and reactive quality checks with a single system that provides comprehensive visibility and proactive controls. The result is a consistent understanding of how data is created, transformed, and consumed, supporting more reliable analytics, faster engineering work, and stronger readiness for AI initiatives.