From Retrieval to Verification: An Agentic Framework for Rule-Aware Engineering Document Compliance
Large-scale engineering projects generate continuous streams of compliance-critical documents, including material submissions, method statements, inspection and test plans, safety data sheets, and contractor certificates. Each must be verified against project specifications, regulatory codes, and contractual requirements. Current manual expert review is slow, inconsistent, and provides limited audit depth. Existing AI approaches typically use Retrieval-Augmented Generation to retrieve relevant clauses but do not conduct structured, rule-bound verification. This paper presents an agentic document verification framework that moves beyond passive retrieval to active, rule-aware compliance checking. The system uses a semantically indexed knowledge base built from project specifications, regulatory standards, and historical approval records. A dedicated Verification Agent decomposes documents into structured claim units, including numerical parameters, referenced standards, tabular test results, and graphical certificates. These are evaluated against dynamically constructed project rule sets using a Chain-of-Thought inference pattern. The framework generates compliance reports with pass, fail, or query verdicts, confidence scores, and traceable evidence bindings for each decision point. Material submissions are the primary validation domain because their dense technical content, cross-referenced tables, graphs, and third-party certificates rigorously test multi-modal parsing and verification capabilities. Validation used an industrial pilot across 10 live projects in the Electrical and Mechanical engineering sector. Results from 63 processed submissions show a 70.9% reduction in average review time, from 52.5 minutes under the existing digital workflow to 15.3 minutes with the AI-assisted system. Results also show an 88% system agreement rate, with human overwrites required in only 12% of verdicts. By incorporating a Propose-Decide-Evidence governance model, the system retains the human engineer as final decision-maker while establishing an efficient, auditable, continuously improving compliance workflow.