A design office drowns in tedious text work: descriptions, specifications, translations, searching documentation. So engineers reach for public AI, sending context and customer data outside. This use case shows one workflow that solves both problems at once: a controlled AI assistant grounded in the company's own documentation, which prepares and organises while the engineer decides. AI narrows, a person decides, and the content never leaves the company.
A machine builder with an after-sales service arm holds full service documentation and years of repair reports, yet on a new ticket the technician still starts from scratch, because nobody searches the archive. RAG over that documentation, narrowed to the specific machine and fault, surfaces similar closed tickets and the right sections of the service manual, and the technician decides. The result: a shorter path to an accurate diagnosis and less dependence on one experienced person.
An automotive supplier runs complaints and nonconformities under the 8D discipline, yet the knowledge inside closed reports stays in a few heads and in an archive nobody searches. AI grounded in that history groups new reports, suggests likely root causes and actions from similar past cases, and the quality engineer decides. The result: a shorter path to D4 and fewer repeats of the same problem.
