Prescptr℠

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.

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Problem Statement

The cost of Unplanned Downtime.

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

Prescriptive maintenance outcomes and plant-ready deployment.

Prescptr pairs measurable maintenance outcomes with deployment choices that work in plant and edge environments.

Outcomes on monitored assets

Unplanned downtime

30–50% reduction on monitored assets.

MTBF

20–30% increase in mean time between failures.

Maintenance cost

Fewer emergency repairs, lower overtime, better spares planning.

Payback

Often in one prevented downtime event per line or asset.

Built for the realities of the plant

Uses your existing sensors

Vibration, temperature, pressure, current, acoustics, process tags.

Integrates with existing systems

SCADA, historians, PLCs, and CMMS.

Runs on-prem or at the edge

Not in a remote data center.

Air-gapped friendly

Operates safely in air-gapped environments.

How Klyff Prescptr Works

From asset signals to earlier maintenance action.

The workflow moves from asset mapping and baseline modeling into early warning, actioning, and continuous refinement.

Asset & Failure Mode Mapping

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).

Data Ingestion & Baseline Modeling

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.

Early Warning & Alerting

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.

Continuous Learning & Optimization

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.

Why Klyff Prescptr for Prescriptive Maintenance

Why teams choose Klyff Prescptr.

Prescptr is built to fit industrial maintenance teams that need earlier signals without changing how plants already operate.

Edge-first architecture

Works where cloud cannot (air-gapped or latency-sensitive environments).

Hardware and vendor agnostic

Use your existing sensors, PLCs, and historians; we don’t force new hardware.

Industrial-grade MLOps

Automated retraining, version control, and monitored drift across hundreds of assets.

Operator-friendly outputs

Clear risk scores and time-to-failure ranges, not data science jargon.

Fast time-to-value

Focus on your top 5–10 critical assets first, then expand.

Examples

Asset classes and environments Prescptr supports.

Prescptr is suited to rotating assets, process-critical equipment, and uptime-sensitive production environments.

Automotive / Metals / Heavy Manufacturing

Critical rotating equipment: motors, pumps, fans, compressors, conveyors, rollers
High-value assets: furnaces, kilns, paint booths, stamping presses
Complex process lines with many interacting variables (temperature, speed, load, pressure)

Food & Beverage / Consumer Goods

High-speed packaging, filling, and bottling lines
Conveyance systems and critical refrigeration/chilling equipment
Pumps, agitators, mixers, and drive trains

Energy & Utilities (where relevant)

Turbines, generators, critical pumps and valves
Continuous process equipment with high uptime requirements

FAQs

Questions before rollout.

SCADA alarms typically use static thresholds (for example vibration > X). Klyff learns patterns over time and recognizes subtle trends—like a gradual rise in vibration at a specific frequency—that indicate upcoming failure before thresholds are breached.

Selected Customer Success Stories

Real customersuccess stories.

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

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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