Knowledge preservation is a critical challenge for small and medium-sized businesses (SMBs). Employee fluctuation and evolving work tasks create a permanent risk of knowledge and experience loss. Therefore, SMBs need effective and efficient strategies for knowledge retention. As most knowledge in companies is primarily encoded as language or text, large language models (LLMs) offer a promising solution for the preservation and utilization of knowledge. However, despite their strengths, their adoption and deployment are challenging. To address this issue, we propose a system based on the Retrieval-Augmented Generation (RAG) concept that combines small, locally run language models with traditional retrieval algorithms to significantly enhance the process of knowledge preservation and utilization by reducing search efforts.