Data engineers often face the challenge of spending more time on repetitive tasks than on innovation due to the complexity of modern data stacks, which involves extensive debugging, testing, and code validation. Datafold addresses these issues by introducing AI-powered automation to reduce manual work and enhance data quality, particularly in tasks like code review, data platform migrations, and root cause analysis for data changes. The company's new features, currently in private beta, include automated pull request summaries that highlight critical changes and their impacts, root cause analysis for data diffs that link specific code changes to data alterations, and a context-aware chat interface for deeper investigation using natural language queries. These AI capabilities are grounded in data validation and aim to enhance efficiency while maintaining data integrity, enabling data engineers to focus more on strategic initiatives and innovation.