Rule-Driven Industrial Document Review with Contextual AI: A Next-Gen Compliance Framework

Ensuring compliance in industrial documentation—particularly with intricate standards like IEC 61508/61511 for functional safety or ISO 21434 for cybersecurity—requires rigorous evaluation against grammatical norms, technical specifications, and document-type conventions. These reviews often demand specialized expertise that is scarce and overstretched in real-world engineering environments. This paper introduces a next-generation, AI-powered compliance framework designed to alleviate this burden by automating the review process for technical documents. The system integrates a multi-layered rule enforcement engine with contextual understanding powered by Transformer-based language models. Three categories of rules—language grammar, domain standards (e.g., IEC, ISO), and document-specific formats—are dynamically enforced through a hybrid approach combining semantic rule retrieval, symbolic reasoning, and document graph analysis. Retrieval-Augmented Generation (RAG) and human-in-the-loop feedback further refine the accuracy of outputs. The system delivers both a comprehensive compliance score and precise, context-aware review suggestions, significantly reducing review effort while preserving expert-level reliability. Experimental results across various industrial documents demonstrate the model’s effectiveness in surfacing complex non-compliances that are frequently overlooked due to human limitations.