Knowledge retention is challenging for companies. Since most knowledge is encoded as text, large language models offer a promising solution for preservation and utilization. A system based on the approach of retrieval augmented generation has been developed. Practical methods for knowledge explication are combined with small, locally executed language models and traditional retrieval algorithms. The article shows the solution approach as well as challenges and promising strategies for implementation.