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Predictive Maintenance Architecture

Predictive Maintenance Architecture

Operations teams targeting equipment reliability within 8-12 weeks

8-12 weeks

Reliability architecture implementation

30-50%

Reduction in unplanned downtime on monitored assets

20-30%

Increase in MTBF

48-72 hrs

Early warnings before failure

What We Do

Predictive maintenance architecture

Sensor strategy, feature engineering, anomaly detection, alerting setup, and privacy-preserving edge design for equipment reliability.

Klyff works with your maintenance and engineering teams to identify critical assets (pumps, motors, bearings, conveyors) and recommend sensor types and locations that maximize early fault detection.

Tri-axial accelerometers near rotating equipment for vibration, temperature probes on housings and fluid lines, and acoustic sensors positioned to detect cavitation, friction, or grinding sounds.

Proper placement and sensor selection ensure that mechanical degradation signatures (imbalance, misalignment, looseness, bearing wear) are captured with a high signal-to-noise ratio, enabling early warnings before catastrophic failure.

Our Engagement

Our engagement model

A clear path from kickoff to production operation, shaped around the service outcome.

01

Sensor integration guidance

02

Model training (2-4 weeks)

03

Deployment (2-4 weeks)

04

Threshold optimization

05

3 months of operational support

Selected Customer Success Stories

Real customersuccess stories.

Explore how teams are using Klyff to improve quality, safety, and operational performance in the field.

Revolutionized critical asset monitoring

Case Study

Revolutionized critical asset monitoring

Revolutionized asset monitoring in cold chain logistics and smart agriculture using Klyff's Edge AI-powered IoT platform for efficiency, sustainability, and real-time insights.

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Optimizing Energy Consumption for a Global Retail Leader with Edge AI

Case Study

Optimizing Energy Consumption for a Global Retail Leader with Edge AI

Discover how a global retail leader reduced energy costs by 15% using Klyff's Edge AI platform for real-time monitoring, anomaly detection, and proactive energy management.

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More Case Studies

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Insights

Our Insights to keep you ahead

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