
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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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
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
A clear path from kickoff to production operation, shaped around the service outcome.
Selected Customer Success Stories
Explore how teams are using Klyff to improve quality, safety, and operational performance in the field.

Case Study
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.
Open case study
Case Study
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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Insights
Connect your first site, deploy your first model, and see measurable ROI in weeks, not months.