Case Study · IoT + Predictive AI

Smart control of air compressors

Proprietary hardware + predictive algorithms that detect failures before they happen. Maintenance based on actual condition, not a calendar.

Sector

Manufacturing

Duration

16 weeks

Lines

Design + AI

Status

In operation

Industrial air compressors

The Challenge

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.

Our Solution

  • Proprietary sensors: pressure, vibration, temperature, current, operating hours. Connected to each compressor with non-invasive installation.
  • Predictive model: AI trained with failure history + real-time patterns predicts failure probability in the next 72 hours.
  • Remote control: start, stop, and load modulation from the dashboard, without having to go down to the machine room.
  • Escalated alerts: WhatsApp to the technician on duty, email to the supervisor, SMS to the manager if no one responds within 10 min.

Measurable Results

87%

Reduction in failures

Unexpected. AI anticipates 72h in advance.

31%

Energy savings

Through load modulation based on actual air demand.

8 → 1.5

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."

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