Build a control plane for AI data operations that tracks datasets from source through pipelines, links each dataset to quality signals, intended model use, data-processing records, and compliance assessments. The tool would provide workflow automation for data mapping, RoPA, DPIAs, dataset approval, and blocker detection across research, training, and production environments.

Foundation model companies are scaling data pipelines while facing rising scrutiny over training data provenance and privacy obligations. The need to unify experimentation velocity with compliance documentation is becoming operationally urgent.