This thesis addresses the problem of onboarding and knowledge retrieval in modern companies, where documentation is often voluminous, generic and fragmented across many systems. Retrieval-augmented generation (RAG) language models combine a search step with text generation: the system retrieves relevant passages from knowledge bases and feeds them to the model to produce more up-to-date and accurate responses. However, traditional RAG systems, based on simple vector or lexical search, struggle with complex questions that require linking information from different domains and synthesising it in a coherent manner. We suggest a RAG framework for industrial settings that combines a structured retrieval approach with a knowledge graph in order to overcome these drawbacks.