A mid-to-large manufacturing enterprise experienced frequent unplanned downtime, escalating maintenance costs, and low asset utilization resulting from reactive and time-based maintenance practices.
By adopting NexaStack’s Agentic Infrastructure Platform, the organization deployed AI agents for predictive maintenance across edge and private cloud environments. These agents continuously monitored equipment health, predicted failures in advance, and autonomously triggered maintenance workflows across CMMS and ERP systems.
The result was a shift from reactive maintenance to autonomous, condition-based maintenance, delivering measurable improvements in reliability, cost efficiency, and operational resilience.
40–50% reduction in unplanned downtime
25–35% reduction in maintenance costs
Extended asset life and higher Overall Equipment Effectiveness (OEE)
Agentic AI Predictive Maintenance utilizes autonomous AI agents to continuously monitor equipment health, predict potential failures, and execute maintenance actions without requiring manual intervention.
Unlike traditional rule-based systems, agentic AI:
Reasons over context and history
Learns continuously from outcomes
Coordinates actions across systems (CMMS, ERP, edge devices)
Unplanned downtime: Sudden equipment failures disrupting production schedules
High corrective costs: Emergency repairs, expedited parts, and external contractors
Inefficient maintenance: Time-based servicing causes over-maintenance or missed failures
Siloed data: Sensor data, logs, and maintenance records scattered across systems
Multi-plant complexity: Diverse assets, legacy systems, and inconsistent processes
Heterogeneous sensor data (vibration, temperature, current, acoustic)
Edge latency and bandwidth constraints
Model drift impacting prediction accuracy
Limited explainability for maintenance engineers
Security and audit requirements across global plants
| Traditional Approach | Limitation |
|---|---|
| Threshold-based alerts | High false positives |
| Manual inspections | Reactive, slow response |
| Centralized analytics | High latency |
| Static ML models | No drift handling |
| Cloud-only deployment | Data sovereignty risks |
NexaStack is the operating system for agentic and reasoning AI, purpose-built to run AI agents securely across edge, private cloud, and sovereign environments.
Unified inference for real-time and batch predictions
Agentic orchestration across monitoring, forecasting, and workflows
Private Cloud & Sovereign AI deployment for sensitive industrial data
Continuous learning pipelines with drift detection
Explainable AI for engineering trust and audits
Using NexaStack, the enterprise deployed a multi-agent predictive maintenance architecture:
Monitoring / SRE Agent
Ingests real-time IoT telemetry at the edge and detects anomalies early
Forecasting / Analyst Agent
Predicts degradation trends, estimates Remaining Useful Life (RUL), and recommends maintenance windows
Workflow / Orchestration Agent
Automatically creates CMMS work orders, triggers ERP procurement, and schedules technicians
Trust & Governance Agent
Ensures explainability, auditability, and compliance
All agents are orchestrated using NexaStack’s unified inference layer, enabling low-latency execution from edge to cloud.
Edge-deployed agents analyze vibration, temperature, acoustic, and current signals
Early anomaly detection before threshold breaches
Context-aware models incorporate load, duty cycles, and operating conditions
Predict failure probability with confidence intervals
Work orders are created automatically in CMMS
Spare parts ordered via ERP
Technician schedules are optimized and pushed to mobile apps
Post-repair data feeds back into models
Drift detection triggers retraining pipelines
Reduced false positives and negatives
Continuous drift detection and retraining
Equipment-specific, domain-aware models
Unified ingestion of IoT telemetry, logs, and maintenance records
Consistent data pipelines across plants
End-to-end automation from detection to execution
Minimal manual intervention
Up to 50% reduction in unplanned downtime
35% lower maintenance and MRO costs
Improved production predictability
Extended asset life and reliability
Real-time inference at the edge
Scalable private cloud AI deployment
Built-in governance and explainability
Manufacturing: CNC machines, robots, motors
Automotive & Aerospace: Engines, turbines, test rigs
Energy & Utilities: Generators, transformers
Oil & Gas: Pumps, compressors
Process Industries: Reactors, agitators
By leveraging NexaStack’s Agentic Infrastructure Platform, organisations can move beyond predictive analytics to agentic action—where AI agents reason, decide, and execute maintenance autonomously.
This use case demonstrates how Agentic AI + Private Cloud AI transforms predictive maintenance into a strategic advantage for Industry 4.0 and smart manufacturing.