Predict Failures
Before They Happen
Telemetry-driven failure prediction and maintenance prioritization—designed to integrate into real workflows and deliver measurable ROI.
- 40% Downtime Reduction
- 3x Typical ROI
- CMMS Integrated
What the Platform Includes
End-to-end predictive maintenance—from sensors to work orders.
Data Foundation
Unified telemetry, maintenance logs, and asset hierarchy.
- Sensor data ingestion
- Maintenance log integration
- Asset hierarchy mapping
- Data quality gates
Condition Monitoring
Real-time KPIs and anomaly detection hooks across asset classes.
- Vibration & thermal monitoring
- Performance degradation tracking
- Threshold-based alerting
- Trend analysis dashboards
Failure Risk Scoring
Asset-class specific prediction models with explainability.
- ML-driven risk scoring
- Remaining useful life estimation
- Root-cause hints
- Confidence intervals
Work Order Automation
CMMS integration with prioritization rules and approval gates.
- Automated work order creation
- Priority-based scheduling
- Parts & resource planning
- CMMS/ERP integration
Analyst Workbench
Explainability, root-cause analysis, and event replay for engineers.
- Model explainability views
- Event timeline replay
- Failure pattern library
- What-if scenario analysis
Monitoring & Governance
Model drift detection, performance tracking, and incident workflows.
- Model drift monitoring
- Alert precision tracking
- Incident response workflows
- Cost impact reporting
Who Is This For?
Manufacturers, energy operators, and fleet managers with critical assets.
Manufacturing Plant Motors
Challenge
Unplanned motor failures causing production line stops and overtime maintenance costs.
Approach
Vibration and current signature analysis with ML-based failure prediction, integrated with SAP PM for automated work orders.
Result
Shifted 80% of maintenance from reactive to planned.
Solar Tracker Systems
Challenge
Tracker motor failures reducing energy yield and requiring expensive field dispatches.
Approach
Telemetry-driven anomaly detection across tracker fleet with prioritized maintenance scheduling.
Result
Early detection of 90% of tracker failures before energy loss.
Heavy Equipment Fleet
Challenge
Unpredictable hydraulic and drivetrain failures causing project delays and safety risks.
Approach
Engine and hydraulic telemetry monitoring with failure risk scoring and parts pre-staging recommendations.
Result
Fleet availability increased from 78% to 94%.
How We Deliver Results
Discovery
Asset inventory, data audit, failure taxonomy, and pilot scope definition
Data Foundation
Telemetry ingestion, maintenance log integration, and baseline models
Pilot
One asset class, validated predictions, CMMS integration, and ROI measurement
Scale
Expand across asset classes and sites with governance and monitoring
Operate
Continuous model tuning, drift monitoring, and cost optimization
System Architecture
From sensor data to automated work orders.
Sensors
Telemetry & Signals
Ingestion
Data Pipeline
Data Store
History & Context
ML Models
Prediction Engine
CMMS/ERP
Work Orders
Human Approval Gates
High-impact actions require human confirmation before execution.
Audit Trails
Full traceability for model recommendations and operator overrides.
Data Quality Gates
Input validation to prevent false alarms from bad data.
Model Governance
Drift detection and performance monitoring with alerting.
What We Need to Start
Asset Registry & Hierarchy
Line → cell → asset structure with equipment metadata.
Telemetry Schema
Sensor types, sampling rates, and signal definitions.
Maintenance History
Failures, parts replaced, work orders, and failure modes.
Business Cost Model
False positive vs false negative cost to calibrate alert thresholds.
How We Measure Success
Downtime Reduction
Decrease in unplanned downtime hours
MTBF Increase
Mean time between failures improvement
MTTR Reduction
Mean time to repair decrease
Planned vs Unplanned Ratio
Shift from reactive to planned maintenance
Alert Precision
Percentage of actionable alerts (low noise)
Maintenance Cost Reduction
Total maintenance spend decrease
Technology Stack
Industry-standard tools for reliable predictive maintenance.
ML & Analytics
Data & Streaming
Edge & Integration
Success Stories
TerraTrak AI Predictive Tracking
Services: Anomaly Detection, Telemetry Analytics
Result: +12% energy generation through predictive optimization.
TerraSmart Solar Asset Monitoring
Services: Condition Monitoring, Field Deployment
Result: 30% faster field deployment with predictive insights.
Abode Smart Home Reliability
Services: Device Health Monitoring, Uptime Optimization
Result: 99.99% uptime & 50% infrastructure cost reduction.
Frequently Asked Questions
Ready to Predict, Not React?
Tell us about your assets. We'll design a predictive maintenance system that delivers measurable ROI.