Abstract: The increasing volume of regulatory documentation in construction and engineering complicates decision-making and raises the risk of errors and noncompliance. Existing search systems fail to provide sufficient semantic understanding of user needs, while large language models suffer from hallucinations and incomplete answers. This paper presents a hybrid intelligent assistant that integrates retrieval-augmented generation, ontological modeling, and multi-agent architecture to address these limitations. The system extracts entities and situations from regulatory documents to build an ontological domain model, enabling both semantic interpretation and automated consistency verification of generated responses. Vector similarity measures are combined with concept-level ontological relationships to improve retrieval accuracy. A multi-agent architecture ensures modularity and scalability, supporting document analysis, query history tracking, personalized response generation based on user context, and precise reference linking to primary sources. The proposed approach is evaluated in the construction domain, where practitioners must simultaneously consider standards at multiple regulatory levels. The practical contribution lies in reducing compliance costs and risks while increasing efficiency in handling complex multi-level regulatory frameworks. Future research will focus on cross-domain implementation and comprehensive evaluation of system performance.

Cite: Grekhova A. , Palchunov D. , Shishkin A. , Zaitsev A.
An intelligent assistant for working with regulatory documents based on ontological modeling and RAG
In compilation IEEE XVII International Conference on Actual Problems of Electronic Instrument Engineering (IEEE APEIE 2025). 2025.

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