A Comparative Analysis of Keyword, Vector, and Hybrid Search Strategies for Enterprise Document Intelligence Platforms

This research presented a comparative analysis of keyword, vector, and hybrid search strategies for enterprise document intelligence platforms. Keyword search remains an important retrieval technique because it provides strong exact matching and is useful for names, identifiers, codes, technical terms, and other situations where precise lexical matching is required. Vector search provides a different capability by focusing on semantic similarity. It can retrieve documents that express similar concepts even when the wording of the query and document is different. This makes it particularly useful for natural-language questions and semantic document discovery. Hybrid search combines keyword and vector retrieval. The reviewed research indicates that this approach can be useful when enterprise queries contain both semantic concepts and exact terminology. Hybrid retrieval can therefore provide a flexible solution for systems that must support different types of user queries. However, hybrid retrieval should not automatically be considered superior in every situation. It introduces additional components, computational requirements, and design decisions. Its effectiveness depends on the dataset, query type, retrieval models, fusion technique, and evaluation method. The overall analysis therefore suggests that the choice of retrieval strategy should be based on the requirements of the particular enterprise application. Keyword search remains suitable for exact retrieval, vector search is valuable for semantic retrieval, and hybrid search provides a balanced approach when both types of retrieval are required. A controlled experiment using the same documents, queries, hardware, and evaluation metrics would be a useful next step. Such an experiment could provide a more direct numerical comparison of the three approaches.

[1]   H. M. Tua and D. Fitrianah, “A lightweight privacy-preserving reranker enables customizable hybrid retrieval for enterprise document search,” Discover Computing, vol. 29, art. no. 89, 2026, doi: 10.1007/s10791-026-09993-z. [2] S. R. Kathi and A. Garg, “Enhancing Enterprise Application Search Using Retrieval-Augmented Generation: A Hybrid Indexing and Reasoning Approach,” European Journal of Information Technologies and Computer Science, vol. 6, no. 1, pp. 13–19, 2026, doi: 10.24018/compute.2026.6.1.70183. [3]  C. Wu and Y. Lin, “Evidence-Unit Fairness and the Limits of Query-Adaptive Sparse-Dense Fusion in Financial Document Retrieval,” 2026. [4]   L. Kapanke, P. Stephan, and R. Mertens, “Transitioning from Classical to Hybrid Search: A Metric-Driven and Explainable Approach,” in 2026 International Conference on Semantic Computing (ICSC), pp. 221–225, 2026, doi: 10.1109/ICSC67292.2026.00037. [5]  B. C., “Semantic Search Engine Using AI Embeddings,” International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, 2026, doi: 10.55041/IJSREM64845. [6]  D. R. Prasad, R. Yegireddi, and P. Anjaneyulu, “A Hybrid RAG Framework for AI Powered Personalized Document Summarization and Contextual Querying,” in 2026 International Conference on Data Science and Business Systems, 2026, doi: 10.1109/ICDSBS69077.2026.11634727. [7]  H. L. Ngoc, “From Keywords to Context: An AI Agent for Natural Language Document Lookup in the Enterprise,” in 2025 RIVF International Conference on Computing and Communication Technologies, pp. 1151–1156, 2025, doi: 10.1109/RIVF68649.2025.11365175. [8]  R. Ruparel and S. Bussari, “Survey and Benchmarking of Retrieval Methods for Enterprise Retrieval-Augmented Generation,” in International Conference on Computer and Applications (ICCA) – Proceedings, 2025, doi: 10.1109/ICCA66035.2025.11430920. [9]  J. Zhu, J. Yuan, K. Yang, X. Chen, S. Yu, H. Lv, Y. Li, and B. Zheng, “An Experimental Evaluation of Hybrid Querying on Vectors,” Proceedings of the VLDB Endowment, vol. 19, no. 2, pp. 183–195, 2025, doi: 10.14778/3773749.3773757. [10]  C. N. Htun and M. P. Khin, “Efficiency Trade-offs and Design Choices in Hybrid Academic Information Retrieval,” in 2025 6th International Conference on Advanced Information Technologies  (ICAIT), 2025,  doi: 10.1109/ICAIT68809.2025.11236772.