The rapid expansion of frontier artificial intelligence (AI) has intensified demand for training datasets that are accurate, representative, traceable, secure, and sufficiently scalable to support increasingly complex model-development pipelines. As data volumes, modalities, sources, and annotation requirements expand, conventional quality-control practices become inadequate for identifying systematic errors, inconsistencies, duplication, contamination, distributional imbalance, and provenance weaknesses before these defects propagate into model behaviour. This study develops a scalable quality assurance framework for improving training-data reliability across frontier AI development. The framework integrates data ingestion controls, schema validation, automated anomaly detection, deduplication, annotation-quality assessment, provenance tracking, distribution monitoring, human review, and continuous quality feedback. Quantitative data-quality indicators are combined with automated validation and risk-based sampling to prioritize high-impact defects while maintaining computational scalability. The architecture further incorporates benchmarking, quality thresholds, dataset versioning, auditability, and governance mechanisms for evaluating reliability across heterogeneous data pipelines. By connecting upstream data-quality assurance with downstream model evaluation, the framework establishes systematic mechanisms for detecting, measuring, and correcting training-data deficiencies. The resulting approach supports reproducible, scalable, and governance-aware data engineering for reliable frontier AI model development.