Achieve Zero Unplanned Downtime with AI in Predictive Maintenance
Lyzr empowers maintenance teams to predict asset failures and reduce downtime. Integrate sensor and CMMS data to move from reactive fixes to proactive control.
- Reduce operational costs
- Predict equipment failures
- Integrate with CMMS
Predictive Operations:
AI-Powered Decisions
Our platform turns your raw sensor and CMMS data into precise failure predictions, automating actions to slash downtime and boost asset reliability across your operations.
01
Data Ingestion
- Connect all sensor, SCADA, and CMMS data for unified time-series analysis.
02
AI Models
- Utilize AI models for accurate anomaly detection and early failure warnings.
03
RUL Forecasting
- Forecast remaining useful life to schedule maintenance at the optimal time.
04
Actionable Insights
- Automate work orders and optimize your spare parts planning and scheduling.
Predictive AI For Your
Assets
Deploy AI across your most critical industrial assets to prevent failures before they impact your production targets and operational budgets.
Rotating Assets
Predict pump and motor failures using vibration and temperature data.
Production Lines
Monitor HVAC and compressor health to plan energy-efficient maintenance.
Utility Systems
Monitor HVAC and compressor health to plan energy-efficient maintenance.
Stop reacting to surprise downtime. Gain confidence with AI-driven failure prediction and prioritized actions.
Real Business Impact of
Predictive AI
- Slash Unplanned Downtime
Maximize asset availability by catching potential failures weeks in advance.
- Cut Maintenance Costs
Shift from costly preventive schedules to efficient condition-based work orders.
- Faster Technician Diagnosis
Provide engineers with explainable AI insights to speed up diagnosis and repair.
- Optimize MRO Inventory
Align spare parts inventory and labor scheduling with predicted asset needs.
Enterprise-Grade Platform
for AI Maintenance
Lyzr provides the core capabilities your reliability engineers need to build, deploy, and manage predictive models at scale across your operations.
Data Connectors
Seamlessly ingest data from your SCADA, IIoT, PLC, and CMMS systems.
Anomaly Detection
Automatically identify subtle deviations from normal equipment operating baselines.
RUL Model Engine
Accurately forecast Remaining Useful Life with confidence intervals for planning.
Explainable AI (XAI)
Translate complex model outputs into maintenance-friendly reason codes for technicians.
Workflow Automation
Trigger and prioritize work orders in your EAM/CMMS based on AI risk scores.
Lyzr vs. The Market:
For AI Maintenance
Lyzr provides a "Bank-in-a-Box" AI framework, ensuring your generative AI banking security matches your most stringent internal standards through total isolation.
Feature
Generic AI Tools
Maintenance SW
Lyzr
Time-series Data
Manual Prep
Limited Support
Native Ingestion
Anomaly Detection
Generic Algorithms
Static Thresholds
Adaptive Baselines
RUL Forecasting
Requires Custom Code
Not Available
Built-in RUL Engine
CMMS/EAM Link
Custom API Work
Native Connection
Bi-Directional Sync
Explainability
Black Box Models
No Insight
Technician Reason Codes
Model Monitoring
Manual Tracking
None
Automated Drift Alerts
No Automation
No Automation
Manual Creation
AI-Triggered Work Orders
Sensor Fusion
Single Source
Siloed Data
Multi-Source Models
Built For Industrial
AI Adoption
Rapid Time-to-Value
Launch your first predictive maintenance pilot in weeks, not months.
Enterprise Security
Get full data governance with robust access controls and detailed audit logs.
Engineer Focused
Our explainable AI and intuitive workflows are designed for reliability teams.
Seamless Integration
Lyzr connects to your existing CMMS, EAM, and IIoT platforms without disruption.
Built Specifically for
Financial Institutions
Join a growing ecosystem of consulting and technology partners
Lyzr gives us the early warnings we need to act before a failure occurs. We have more confidence in our maintenance scheduling and have reduced unplanned downtime by over 30%. It's a complete game-changer for our reliability engineering team and operational efficiency.
Head of Ops
Global Manufacturing Inc.
Zero
Data Exfiltration Incidents
Deploy AI in Predictive Maintenance
in 4 Steps
- Step - 1
Connect Data
Link your sensor, SCADA, and CMMS/EAM data sources to define your assets.
- Step - 2
Train AI Models
Build initial anomaly detection and RUL forecasting models from historical data.
- Step - 3
Validate & Tune
Test and calibrate predictive alert thresholds with your reliability engineers.
- Step - 4
Automate & Monitor
Push automated work orders to your CMMS and continuously monitor for model drift.
Frequently asked questions
It uses machine learning to analyze data from industrial assets, predict when equipment failure might occur, and estimate its remaining useful life (RUL). This allows teams to perform maintenance at the precise moment it is needed, preventing unplanned downtime and reducing operational costs.
By detecting subtle anomalies in sensor data that precede a failure, AI provides early warnings. This gives maintenance teams days or weeks to schedule repairs proactively, turning a potential emergency into a planned, non-disruptive work order, thus improving asset availability.
Effective models typically require time-series data from sensors (vibration, temperature), operational data from SCADA systems, and historical maintenance records from a CMMS. The more comprehensive the data, the more accurate the failure predictions will be for your teams.
AI predictive maintenance is an evolution of condition-based maintenance. While CBM reacts to preset thresholds, AI predicts future failures by identifying complex patterns and estimating the remaining useful life, offering a much longer planning horizon for all your teams.
Trigger and prioritize work orders in your EAM/CMMS based on AI risk scores.
Yes, Lyzr is designed for seamless integration. Our platform connects with leading CMMS and EAM systems to pull historical data and automatically create and prioritize work orders based on AI-driven risk scores, streamlining your entire maintenance workflow for everyone.
Anomaly detection identifies when a piece of equipment deviates from its normal operating baseline. Failure prediction goes a step further by using these anomalies and other patterns to forecast the probability of a specific failure mode and estimate when it's likely to occur.
A typical Lyzr pilot takes 6-8 weeks. It requires access to historical sensor and maintenance data for a set of critical assets, along with collaboration from your reliability engineers to validate the model's alerts and tune its performance before full-scale deployment.
Our platform includes built-in MLOps capabilities to continuously monitor model performance. It automatically detects model drift caused by new operating conditions or asset aging and alerts your team when a model needs to be retrained to maintain its predictive accuracy.
ROI is measured by tracking key metrics like the reduction in unplanned downtime, decreased maintenance costs from eliminating unnecessary tasks, lower MRO inventory holdings, and improved overall equipment effectiveness (OEE). Lyzr provides dashboards to track these improvements.
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