
Case Study · IoT + Predictive AI
Proprietary hardware + predictive algorithms that detect failures before they happen. Maintenance based on actual condition, not a calendar.
The client had 8 air compressors critical to the production line. An unexpected failure meant halting production for 6-8 hours while the brand's technician arrived. Maintenance was calendar-based: changes every 500 hours — but some parts lasted 2,000 hours and others failed at 300.
Reduction in failures
Unexpected. AI anticipates 72h in advance.
Energy savings
Through load modulation based on actual air demand.
Downtime hours/month
Planned maintenance instead of reactive.
"Before, the compressor would die and we would start calling. Now, the screen tells us what is going to happen on Tuesday."