Prescriptive Maintenance for Infinite Uptime
Turn vibration, temperature, and process data into early warnings & recommendations, so you fix equipment on your schedule, not when it fails.
Prescptr helps maintenance teams act earlier with on-prem or edge-based intelligence built around existing plant signals.
Problem Statement
Klyff Predictive Maintenance converts your existing sensor and process data into reliable early warnings, often 48–72 hours before failure.
Unplanned outages: $10K–$100K per hour in lost production, labor, and overtime
Over-servicing equipment: Time-based PM that replaces parts too early “just in case”
Scattershot data: Sensors, SCADA, and PLCs collecting data that no one has time to analyze
Firefighting culture: Maintenance teams living in reactive mode instead of planned work
Klyff Prescptr Delivers
Prescptr pairs measurable maintenance outcomes with deployment choices that work in plant and edge environments.
30–50% reduction on monitored assets.
20–30% increase in mean time between failures.
Fewer emergency repairs, lower overtime, better spares planning.
Often in one prevented downtime event per line or asset.
Vibration, temperature, pressure, current, acoustics, process tags.
SCADA, historians, PLCs, and CMMS.
Not in a remote data center.
Operates safely in air-gapped environments.
How Klyff Prescptr Works
The workflow moves from asset mapping and baseline modeling into early warning, actioning, and continuous refinement.
Identify critical assets by downtime cost and failure history.
Define relevant failure modes: bearings, seals, pumps, fans, motors, gearboxes, furnaces.
Map available sensors and data sources (SCADA, PLC, historian, logs).
Ingest historical data where available; otherwise start streaming live signals.
Establish a “healthy baseline” for each asset under normal operating condition.
Train anomaly detection and degradation models tailored to each asset type.
Deploy models at the edge or on-prem servers.
Generate actionable alerts with lead time (for example “bearing degradation likely within 72 hours”).
Intelligent action to elongate the downtime timeline (reducing speed by 10% will extend its life until the weekend).
Integrate with your CMMS or work order system to automatically create maintenance jobs.
Confirm true positives and false positives with your maintenance team.
Use feedback loops to refine models and thresholds.
Extend coverage to more assets and lines as value is demonstrated.
Klyff Prescptr for Prescriptive Maintenance
Prescptr is built to fit industrial maintenance teams that need earlier signals without changing how plants already operate.
Works where cloud cannot (air-gapped or latency-sensitive environments).
Use your existing sensors, PLCs, and historians; we don’t force new hardware.
Automated retraining, version control, and monitored drift across hundreds of assets.
Clear risk scores and time-to-failure ranges, not data science jargon.
Focus on your top 5–10 critical assets first, then expand.
Examples
Prescptr is suited to rotating assets, process-critical equipment, and uptime-sensitive production environments.
FAQs
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.
