Enhancing Enterprise Application Search Using Retrieval-Augmented Generation: A Hybrid Indexing and Reasoning Approach

The search for enterprise applications is a difficult issue because the operational artifacts (source code, configuration file, logs, ticketing, and technical documentation) are heterogeneous, dynamic, and loosely structured. Conventional enterprise search systems based on both keyword matching and heuristic ranking do not offer semantic intent, contextual dependencies, and cross-artifact relationships, resulting in limited relevancy and suboptimal decision support. Although large language models (LLMs) can be used to make good decisions, their direct use in enterprise search is limited because of the risk of hallucinations, absence of domain bases, and demands of governance. This study proposes a hybrid framework of an enterprise search system that balances both retrieval-augmented generation (RAG) and sparse and dense indexing systems to co-optively optimize the quality of retrieval and the rate of reasoning. The design uses a combination of a classical inverted index with embedding-based vector retrieval to find contextually relevant evidence and use it to generate grounded and traceable outputs with an LLM. In contrast to conversational RAG systems, the proposed approach is application-centric and further tailored to enterprise workflows, including access control, latency, and incremental deployment concerns. Real-world enterprise application data evaluation demonstrates that the proposed framework enhances top-k retrieval accuracy by 1825%, workflow task completion accuracy by 2100%, and hallucinated responses by approximately 40% over conventional keyword-based search baselines. These findings indicate that hybrid retrieval-and-reasoning systems based on RAG can be useful as scalable, efficient, and governance-conformist improvements to enterprise application search.