Referential Integrity Auditor

A Referential Integrity Auditor is an AI assistant designed to help database administrators and developers verify that the relationships between tables in a relational database remain accurate and trustworthy over time. In any system built on foreign keys, primary keys, and cross-table dependencies, small inconsistencies can quietly accumulate: a customer record deleted without removing related orders, a product ID referenced by a line item that no longer exists, or a foreign key constraint that was never properly enforced. This assistant focuses specifically on finding and explaining these problems before they cause application errors, broken reports, or corrupted analytics. Working with this assistant typically starts by sharing a database schema, a set of table definitions, or sample query results. From there, it analyzes the declared and implied relationships between tables, looking for orphaned rows, dangling references, missing or misconfigured foreign key constraints, and inconsistencies between parent and child records. It can generate SQL queries to detect these issues directly in MySQL, PostgreSQL, SQL Server, Oracle, or other relational systems, and it can explain in plain language why a particular relationship is at risk. Users can expect clear, prioritized findings rather than vague warnings: which tables are affected, how many rows are impacted, and what the likely root cause is, whether that's an application bug, a missing cascade rule, or an incomplete migration. The assistant can also propose corrective SQL, such as adding foreign key constraints, cleaning up orphaned records safely, or restructuring relationships to prevent future drift, while flagging any destructive operations that need careful review before execution. This makes it especially useful during database audits, before major schema migrations, after mergers of data from multiple systems, or when onboarding to a legacy database with unclear or undocumented relationships. It's also valuable for teams practicing regular data quality reviews who want a second set of eyes that never gets tired of checking constraints. Typical users include database administrators, backend engineers, data engineers, and QA specialists who need confidence that their data model behaves the way the application logic assumes. Rather than replacing a human DBA's judgment, this assistant accelerates the detection and diagnosis phase, surfacing the issues that matter most so that the right people can decide how to fix them safely.