Connecteurs sémantiques propriétaires

An ML-powered maintenance prediction system reducing vehicle downtime and optimizing fleet performance with accurate health analytics.

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Case detail
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Project challenges

Analyzing real-time telemetry and diagnosing faults ahead of failures required precision and rapid alerts across large fleets.

  • Processing continuous IoT data streams.
  • Early prediction of mechanical failures.
  • Integrating seamlessly with fleet management tools.
  • Keeping operational alerts simple for field teams.
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The solution

We built a predictive maintenance layer that analyzes behavior patterns and alerts teams before issues escalate — improving asset lifespan and performance.

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The result

Fleet operators reduced breakdown events and maintenance cost significantly.

60

%

Decrease in breakdowns

25

%

Lower maintenance

99

%

Fault detection alerts

Project information

Date:
May 18, 2026
Client:
DriveLogistics India
Industry:
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Services:
IoT + AI Analytics, Dashboard Design
Technology stack:
Python, Kafka, AWS IoT, React

“This solution made fleet health visible like never before.”

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Liam Parker
Operations Lead
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call & introduction
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NOus contacter

Notre méthodologie
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Meeting d'introduction de 30min
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Cadrage d'une mission de conseil
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Opérationnalisation

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