Automating Compliance Workflows in Aerospace Systems: A Scalable Framework for Specification Review and Requirement Mapping

Compliance verification in aerospace systems often relies on labor-intensive workflows that demand extensive manual effort to produce structured review documentation and requirement matrices. These processes can span dozens of hours per review, are vulnerable to inconsistencies due to non-standardized annotations, and depend heavily on individual interpretation of fragmented technical sources. With a growing backlog of review tasks and a steady influx of new requests, the need for scalable automation has become increasingly urgent. This study presents a modular automation framework designed to streamline compliance assessments through intelligent document parsing, requirement extraction, and matrix generation. The system integrates optical character recognition, computer vision, and natural language processing techniques to process both scanned and digital documents. By digitizing data across multiple hardware configurations and automating extraction from diverse technical records, the framework enables consistent evaluation across a broad spectrum of requirement categories. Automation scripts and standardized templates facilitate rapid population of compliance matrices, reducing manual hand-offs and minimizing reliance on specialized expertise. Implementation led to a 39% reduction in turnaround time and a 62% increase in monthly throughput, demonstrating measurable efficiency gains in the compliance review process. This framework exemplifies how intelligent automation can drive operational efficiency, deliver measurable cost savings, and pioneer data-driven and scalable innovation in aerospace compliance engineering.