Industrial machinery manufacturers handle thousands of after-sales support requests each day, from malfunction reports to spare-part inquiries. Responses often rely on manual searches through extensive documentation and the tacit knowledge of senior technicians. As a result, information remains fragmented, leading to slow resolution times, inconsistent service quality, and gradual loss of expertise. Existing AI solutions, such as chatbots and Retrieval-Augmented Generation (RAG) systems, have shown promise in automating customer support but remain inadequate for industrial contexts. They typically operate as generic assistants with limited domain adaptation, lack integration with company workflows, and provide no measurable confidence in their outputs. In environments where incorrect suggestions can cause costly downtime or safety issues, these limitations hinder their adoption in production settings.