Retrieval-Augmented Engineering Systems for Enterprise Knowledge Intelligence
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I2P109Keywords:
Rag Systems, Enterprise Knowledge Retrieval, Vector Search, Ai Reasoning, Semantic InfrastructureAbstract
Modern enterprises generate massive amounts of structured, semi-structured, and unstructured data across distributed environments. Traditional enterprise knowledge systems often face challenges such as limited contextual understanding, fragmented information retrieval, poor scalability, and weak integration with AI-driven decision-support systems. Retrieval-Augmented Generation (RAG) architectures have emerged as an advanced solution by combining semantic retrieval, vector databases, large language models (LLMs), and contextual reasoning frameworks to improve enterprise intelligence. This paper introduces Retrieval-Augmented Engineering Systems (RAES), a next-generation enterprise intelligence framework that integrates semantic retrieval, contextual augmentation, engineering analytics, and generative AI to enhance organizational decision-making, operational efficiency, cybersecurity resilience, and business intelligence automation. The proposed architecture includes multiple layers such as data ingestion, semantic transformation, vector embedding generation, contextual retrieval, reasoning orchestration, and intelligent response synthesis. It also incorporates governance mechanisms including metadata intelligence, role-based access control, cybersecurity monitoring, and observability-driven analytics for scalable enterprise deployment. The study evaluates hybrid retrieval pipelines using transformer embeddings, dense vector retrieval, metadata-enhanced indexing, semantic reranking, and adaptive orchestration techniques on enterprise engineering datasets including technical documentation, incident logs, design specifications, and software lifecycle repositories. Performance metrics include retrieval precision, contextual relevance, latency reduction, semantic similarity, operational scalability, and decision-support accuracy. Experimental results demonstrate that retrieval-augmented engineering systems outperform traditional enterprise search systems and standalone generative AI models by improving contextual retrieval accuracy, reducing hallucinations, increasing engineering productivity, strengthening operational traceability, and enhancing governance capabilities. The research concludes that retrieval-augmented engineering systems represent a transformative approach for building intelligent, scalable, secure, and governance-driven enterprise knowledge ecosystems that support modern digital enterprises and future Industry 5.0 environments.
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