Organizations leveraging PostgreSQL for transactional and analytical workloads often encounter data quality issues such as schema drift, missing values, and inconsistent records, leading to unreliable reporting and decision-making. DQLabs integrates with PostgreSQL to provide AI-driven data quality monitoring and anomaly detection across the entire data lifecycle. Before data enters PostgreSQL, DQLabs validates its accuracy, completeness, and consistency, preventing low-quality data from polluting the database. Once ingested, DQLabs continuously tracks key data health metrics, automatically detecting anomalies like outliers, duplicates, and schema changes that could impact downstream applications. With built-in data governance enforcement, DQLabs ensures adherence to business rules and compliance standards while providing end-to-end visibility into data transformations. This integration empowers organizations to maintain high-quality, trusted data within PostgreSQL, enabling accurate analytics, streamlined reporting, and optimized operational workflows.