The Challenge
If you've ever worked in a factory, you know how it goes. One machine stalls, and suddenly, everything's on hold. That's exactly what was happening at our plants—unexpected breakdowns were eating up hours and costing serious money. The maintenance team was constantly in crisis mode, and the mood on the floor was frustrated and burned out.
Our Approach
We didn't come in with a plug-and-play solution. We listened, spent time on the line, and learned the patterns and pain points. Then we built an AI system powered by real-time sensor data and years of maintenance logs. It didn't just throw out predictions—it showed its work and suggested what to do next. Operators could challenge it, adjust it, or even teach it new things.
The Implementation
We started where things broke down most often, running the tool side-by-side with the old way. Some folks were skeptical, but once the AI predicted a failure before it happened, people started paying attention. Training happened on the floor, with real machines and real consequences.
The Results
Unplanned downtime dropped, output per shift jumped, and the team was fired up. Leadership called it the most meaningful shift in company culture in over a decade, and people felt like they were finally in control, not just reacting to chaos.
