MANUFACTURING
We build for lines that can't afford to stop.
Nikaxu builds AI systems for manufacturers running tight production schedules — integrated into changeovers, quality checks, and maintenance windows already in place.
Our work is shaped by a line where one hour of downtime costs the whole shift its target
An hour of unplanned downtime on one line pushes output targets for the whole shift, and delays every order that depended on it. That's the risk Nikaxu tests for on one line before a system reaches the rest of the operation. A data assessment establishes whether the data across shifts and lines is even there to build on. A proof of concept runs against one line or one shift, not the whole operation, so a hypothesis gets tested without risking output that's already running to target. Full deployment happens only once that's proven, and only where you decide to extend it, one line at a time.
KNOWLEDGE RETRIEVAL
Repair times dropped 10–25% through faster access to validated instructions and historical repairs
Service technicians spend hours each week searching across manuals, standards, and historical repair logs for the right piece of information. The cost shows up in longer repair times, inconsistent quality, slow onboarding, and heavy dependence on senior technicians whose knowledge walks out the door at retirement. The information exists. It just isn't retrievable fast enough at the moment a technician is standing in front of the equipment. An AI service agent unifies the technical knowledge base: manuals, standards, past repair records, resolution databases. It answers technician questions in plain language, reading the operational context: which piece of equipment, what symptoms, what's already been tried. Answers reference the source material, so the technician can verify rather than trust blindly. The system is built around the client's existing knowledge sources.
How much of that knowledge walks out the door when a senior technician retires?