We built semantic issue detection for SQL

As companies generate increasing amounts of SQL due to the rise of cloud data warehouses and tools like dbt, they face challenges in maintaining complex codebases, which often result in reduced development velocity and poor data quality. This complexity leads to semantic bugs, where SQL code executes correctly but produces incorrect data, posing significant challenges to data teams similar to those faced in traditional software development. Foundational addresses these issues by leveraging static code analysis and column-level data lineage to detect semantic bugs at the pull request stage, enabling data developers to move quickly while maintaining high data quality. This approach helps identify potential errors before they enter production, reducing the risk of data incidents and allowing teams to deploy changes efficiently with greater confidence.