Anomalo has expanded its data quality platform to handle unstructured data monitoring for enterprise AI applications. As reported by Sean Michael Kerner for VentureBeat, the new solution aims to reduce AI deployment time by 30% through improved data quality control. The platform adds structured metadata to unstructured documents, helping organizations identify sensitive information and data quality issues before feeding content into AI models. Key features include custom issue detection, support for private cloud models, and metadata tagging. The company also announced a $10 million extension to its Series B funding, bringing the total to $82 million. CEO Elliot Shmukler emphasizes that poor data quality often leads to AI project abandonment, and their solution helps enterprises better understand and curate their data while ensuring compliance and risk mitigation.