This study introduces KriRAG, a novel Retrieval-Augmented Generation (RAG) architecture designed to assist criminal investigators in analyzing information and overcoming the challenge of information overload. KriRAG structures and summarizes extensive document collections based on existing investigative queries, providing relevant document references and detailed answers for each query. Working with unstructured data from two homicide case files comprising approximately 3,700 documents and 13,000 pages, a comprehensive evaluation methodology is established, incorporating semantic retrieval, scoring, reasoning, and query response accuracy. The system’s outputs are evaluated against queries and answers provided by criminal investigators, demonstrating promising performance with 97.5% accuracy in relevance assessment and 77.5% accuracy for query responses. These findings provide a rigorous foundation for other query-oriented and open-ended retrieval applications. KriRAG is designed to run offline on limited hardware, ensuring sensitive data handling and on-device availability.