CLIFFGATECLIFFGATE · Industrial AI
Industrial AI: Engineer knowledge assistant
LLM-based search and decision support for maintenance, process, and incident teams
Бизнес-задача
Engineering knowledge is usually scattered across PDFs, manuals, equipment passports, incident reports, shift logs, regulations, emails, and the memory of experienced specialists. When a non-standard situation occurs, time is lost searching for the right instruction and past cases.
Технический подход
- —Build a controlled knowledge base from manuals, procedures, equipment documentation, repair history, incident journals, and internal regulations.
- —Use retrieval-augmented generation so the assistant answers from approved sources rather than uncontrolled memory.
- —Allow engineers to describe a symptom in natural language and receive likely causes, relevant procedures, similar historical incidents, and recommended checks.
- —Apply access control so sensitive operational, safety, or financial information is only visible to authorized roles.
- —Log questions and answers to identify knowledge gaps and improve documentation.
Бизнес-результат
- —Faster troubleshooting and lower dependency on a small number of senior engineers.
- —More consistent decisions across shifts and locations.
- —Shorter onboarding time for new technical staff.
- —Better use of historical incidents and maintenance records.
- —A living knowledge system that improves as the company operates.