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
Learn how Klyff deployed an on-device continual learning loop on ARM Cortex-M7 gateways, achieving a 40% reduction in false positives and automatically adapting to machine aging without cloud retraining.
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Case Study
Discover how Klyff deployed quantized CNNs on Hailo-8 edge accelerators at the glass level, dropping inspection latency from 120ms to 8ms and saving an estimated $12,000/month per line in rework costs.
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Case Study
See how Klyff deployed an ATEX-certified, privacy-by-design safety agent that localizes and scrubs human faces at the source, reducing safety incidents by 34% with zero PII leaving the edge.
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Insights
Connect your first site, deploy your first model, and see measurable ROI in weeks, not months.
