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Energy & manufacturing · Predictive AI

Predictive maintenance for a global energy leader

A major energy conglomerate

70%improvement in machine reliability
70%

Better machine reliability

2×

Lower maintenance costs

100s

Of sensors monitored

The challenge

Unplanned downtime was a major drain. Hundreds of sensors produced a constant stream of data, but engineers had no way to spot early signs of fatigue or prioritise maintenance by actual machine health.

What we built
  • A predictive maintenance framework turns high-frequency sensor data into early warnings.
  • Real-time monitoring produces a health score for every asset.
  • Reliability reports help engineers prioritise work by risk.
  • Maintenance shifted from reactive and scheduled to predictive.
The results
  • Machine reliability improved 70%.
  • Maintenance costs were cut in half.
  • Operations moved from firefighting to foresight.
Similar challenge?

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