Scaling Regulatory Compliance: A Multi-Agent System with Multimodal RAG for Automated Electrical Installation Inspection Under NOM and NEC Standards

Abstract

In electrical systems, it is important to comply with regulations that guarantee the safety and proper functioning of the installation; to validate that this is complied with, it is necessary to have certifications that are carried out by inspectors who make a visual review of the electrical installations. This article presents a multi-agent artificial intelligence system based on multimodal Generation Augmented by Recovery (RAG) that verifies compliance with electrical standards. The system is made up of agents specialized in visual perception, automatic retrieval of the applicable standards and the drafting of a technical opinion; this is done based on image processing contrasted with the NOM and NEC standards mainly in conjunction with some complementary standards such as NMX. The validity of the functionality of the system was tested in real environments where 103 inspections were carried out, achieving a reduction in the time used for inspections, which dropped from the usual 18.4 h to only 7.3 min, the time required for the inspection using the system, which represents an improvement of 99.3% in time efficiency. On the other hand, consistency among inspectors (kappa Cohen) increased from 0.68 to 0.94, thus demonstrating that there is a high standardization in opinions. These results show that the integration of large-scale language models (LLMs) and multi-agent architectures not only improved the productivity of inspection processes but also gives greater certainty to a good assessment of the physical conditions in electrical installations.