Your AI Predictive Maintenance Teammate analyzes equipment sensor data — vibration, temperature, pressure, and current — to predict failure windows, schedule maintenance proactively, manage spare parts inventory, and eliminate unplanned downtime across your facilities.
From sensor data ingestion to maintenance work order generation, your AI teammate handles every step of the predictive maintenance lifecycle.
Ingests and analyzes vibration, temperature, pressure, current, and acoustic data from equipment sensors in real time — detecting anomalies invisible to human operators.
Uses machine learning models trained on your equipment history to predict failure windows with 95% accuracy — giving maintenance teams days or weeks of advance notice.
Automatically generates maintenance work orders and schedules them during planned downtime windows — minimizing production impact while preventing failures.
Predicts spare parts requirements based on failure forecasts and consumption patterns — ensuring critical parts are available when needed without excess inventory.
Assigns real-time health scores to every monitored asset — providing maintenance teams with a prioritized view of equipment condition across the entire facility.
Tracks MTBF, MTTR, maintenance costs, and prevented failures — quantifying the ROI of predictive maintenance and surfacing continuous improvement opportunities.
Designed for maintenance, operations, and reliability leaders responsible for equipment availability, cost control, and safety.
Senior maintenance leaders accountable for equipment uptime, maintenance budgets, and team productivity — seeking AI that transforms reactive maintenance into predictive intelligence.
Operations executives responsible for plant-wide performance, asset utilization, and production targets — needing AI that maximizes equipment availability across facilities.
Technical leaders driving reliability programs, FMEA analysis, and equipment lifecycle management — seeking data-driven insights to improve asset reliability.
AI Predictive Maintenance directly displaces costly reactive maintenance — preventing failures instead of responding to them.
Every AI Predictive Maintenance deployment is benchmarked against the metrics that matter most to maintenance and operations leaders.
Predictive failure detection eliminates surprise breakdowns — keeping production lines running and delivery commitments met.
Condition-based maintenance extends equipment life by addressing degradation before it causes failure.
Pre-failure diagnosis means maintenance teams arrive prepared with the right parts and procedures — cutting repair times dramatically.
Shifting from reactive to predictive maintenance eliminates emergency repair premiums and reduces overall maintenance spend.
Machine learning models trained on your equipment data achieve 95%+ accuracy in predicting failure events and timing.
Demand-driven parts forecasting ensures critical spares are in stock when needed — without excess inventory carrying costs.
Your AI teammate integrates with the maintenance, IoT, and asset management systems you already use — no rip-and-replace required.
Native integration with SAP Plant Maintenance for work order creation, equipment master data, and maintenance history tracking.
Bi-directional sync with Maximo for asset management, work order workflows, and maintenance scheduling optimization.
Connects to Siemens IoT platform for sensor data ingestion, edge analytics, and digital twin integration.
Integrates with process data historians for time-series sensor data, trending, and historical analysis across equipment fleets.
Connects to PTC IoT platform for real-time equipment monitoring, augmented reality work instructions, and remote diagnostics.
Standard industrial IoT protocols for sensor data collection, PLC connectivity, and edge device integration — fully extensible.
A structured, phased deployment that delivers measurable maintenance improvements from the first sprint.
Every prediction is auditable. Every work order is governed. Built for safety-critical manufacturing environments.
Every critical maintenance decision routes to a maintenance supervisor. AI predicts and recommends — technicians retain authority over equipment interventions.
Full prediction audit trails, role-based access, and maintenance logging designed for ISO 55000 asset management compliance.
Data residency controls, encryption at rest and in transit, and configurable retention policies for sensor data and maintenance records.
Connects to SAP PM, IBM Maximo, Siemens MindSphere, PTC ThingWorx, and custom IoT platforms via secure APIs.
See measurable results — less downtime, lower costs, and higher equipment reliability — within your first month. No multi-year commitment. Just results.