Manufacturers of industrial machinery face a persistent challenge in managing technical knowledge for after-sales support. Expertise remains dispersed across unstructured email exchanges and poorly organized documentation, leading to slow responses, inconsistent service quality and progressive loss of know-how. While recent advances in Retrieval-Augmented Generation (RAG) have enabled document-grounded assistants, current systems struggle to capture the causal dependencies linking problems, causes and solutions that support technical reasoning. Moreover, causal extraction methods are hindered by limited annotated data and insufficient integration with external knowledge bases, often resulting in uncontrolled hallucinations and weak factual grounding. This thesis addresses these limitations through two complementary contributions.