Research corpus quality certification system with a modular architecture spanning intake, core certification engine, operator management, and reporting layers. Data flows from corpus repositories and external systems through API ingestion, WAF security, and load balancing into a DAG-based quality validation engine that evaluates five core dimensions: deduplication rates, security compliance, accuracy, sampling pass rates, and annotation consistency. The control hub orchestrates task scheduling, resource allocation, and parallel operator execution across a managed compute layer with elastic scaling and fault recovery. Certified results aggregate through a reporting module that generates quality scores, archives findings, and feeds insights back to source systems and corpus management platforms via structured data lifecycle tracking and audit logging.