Stop Predicting Failures. Start Preventing Them with AI.
Eliminate costly unplanned downtime. Transform your maintenance strategy from reactive to predictive with our custom AI solutions, turning your operational data into your most powerful asset for reliability and efficiency.
Why Partner with Errna for Predictive Maintenance?
We go beyond off-the-shelf tools. We build strategic, custom AI solutions that integrate seamlessly into your operations, delivering tangible ROI by transforming your maintenance from a cost center into a competitive advantage.
Custom AI Models
Generic models yield generic results. We develop bespoke AI algorithms trained specifically on your machine data and operational context, ensuring unparalleled accuracy in failure prediction.
End-to-End Expertise
From data readiness assessments and IoT integration to model deployment and ongoing optimization, our multi-disciplinary team of data scientists, engineers, and domain experts manages the entire lifecycle.
Measurable ROI Focus
Our solutions are designed with business outcomes in mind. We establish clear KPIs from day one, focusing on reducing downtime, lowering maintenance costs, and increasing Overall Equipment Effectiveness (OEE).
Seamless System Integration
An AI insight is only valuable if it's actionable. We specialize in integrating our predictive models with your existing CMMS, EAM, and ERP systems to automate work orders and streamline workflows.
Deep Industry Knowledge
We speak your language. Our experts have hands-on experience in manufacturing, energy, logistics, and other asset-heavy industries, allowing us to understand your unique challenges and opportunities.
Scalable & Secure Platform
Built on enterprise-grade cloud or edge infrastructure, our solutions are designed to scale from a single production line to your entire global operation, with security at the core of our architecture.
Your Extended AI Team
You don't need to hire a team of data scientists. We act as a seamless extension of your in-house team, providing the specialized AI talent you need to succeed without the overhead.
Proven CMMI 5 Process
Our CMMI Level 5 appraised processes ensure predictable, high-quality delivery. We de-risk your AI initiatives through a mature, structured, and transparent development and deployment methodology.
AI-Augmented Delivery
We use AI to build AI. Our proprietary development platforms and MLOps practices accelerate project timelines, improve model performance, and ensure your predictive maintenance solution delivers continuous value.
Our Comprehensive AI for Predictive Maintenance Services
We offer a full spectrum of services to guide you at every stage of your journey, from initial strategy to full-scale deployment and continuous improvement.
Predictive Maintenance Strategy & Roadmap
We collaborate with your stakeholders to define a clear vision and actionable roadmap. We align the predictive maintenance initiative with your core business objectives, ensuring buy-in and a clear path to value.
- Identify high-impact assets for pilot projects to demonstrate quick wins.
- Develop a comprehensive business case with projected ROI and cost-benefit analysis.
- Create a phased implementation plan that minimizes disruption and maximizes learning.
Sensor & IoT Data Integration
Your data is the fuel for AI. We build robust, scalable data pipelines to collect, process, and unify data from diverse sources like SCADA, PLC, historians, and IoT sensors, creating a single source of truth for your assets.
- Connect to real-time streaming data for immediate insights.
- Integrate historical maintenance records and operational logs for richer context.
- Ensure data quality and integrity through advanced cleansing and validation techniques.
Custom AI/ML Model Development
We design and build machine learning models tailored to the unique failure modes of your equipment. Using techniques from time-series analysis to deep learning, we create highly accurate predictive engines.
- Develop models that predict specific failure types, not just general anomalies.
- Optimize algorithms for performance, whether in the cloud or on edge devices.
- Provide full transparency into model logic and performance metrics.
Anomaly Detection & Failure Pattern Recognition
Our AI systems act as a vigilant, 24/7 watchtower over your assets. They learn the normal operating behavior of each machine and instantly flag subtle deviations that are precursors to failure, often weeks or months in advance.
- Detect subtle changes in vibration, temperature, pressure, and other sensor readings.
- Minimize false positives with sophisticated, self-learning algorithms.
- Identify novel or previously unseen failure patterns as they emerge.
Remaining Useful Life (RUL) Prediction
Go beyond knowing *if* a component will fail to knowing *when*. Our RUL models provide a time-based forecast of component lifespan, enabling you to schedule maintenance just-in-time, maximizing asset utilization and minimizing costs.
- Get a clear, data-driven window for optimal maintenance intervention.
- Optimize spare parts inventory based on accurate RUL forecasts.
- Plan capital replacement strategies with greater confidence.
Root Cause Analysis (RCA)
When a failure is predicted, our AI helps answer the crucial "why?". By analyzing correlated factors and historical data, our systems can pinpoint the likely root causes, helping you move from fixing symptoms to solving underlying problems.
- Identify contributing factors like operational parameters, material batches, or environmental conditions.
- Reduce recurring failures by addressing their fundamental causes.
- Improve equipment design and operational procedures based on RCA insights.
Prescriptive Maintenance Recommendations
This is the next frontier beyond prediction. Our advanced models don't just tell you what's wrong; they recommend the specific actions to take, suggesting the most effective and efficient maintenance procedures to resolve the impending issue.
- Receive prioritized, actionable recommendations for your maintenance teams.
- Link predicted failures directly to specific standard operating procedures (SOPs).
- Optimize maintenance response by suggesting the right skills and parts for the job.
Integration with CMMS/EAM/ERP Systems
We bridge the gap between AI insights and operational execution. By integrating with systems like SAP PM, IBM Maximo, or Infor EAM, we can automatically generate work orders, allocate resources, and update asset records based on AI predictions.
- Automate the creation of maintenance requests and work orders.
- Enrich your asset management systems with real-time health data.
- Create a closed-loop system where maintenance feedback improves AI models.
Real-time Monitoring Dashboards
We provide intuitive, role-based dashboards that visualize the health of your critical assets at a glance. From the plant floor to the boardroom, everyone gets the insights they need, when they need them, in a format they can understand.
- Track real-time asset health scores and RUL estimates.
- Receive configurable alerts via email, SMS, or mobile app.
- Drill down from a high-level overview to specific sensor readings and model outputs.
Digital Twin Development & Simulation
We create dynamic, virtual replicas of your physical assets. These digital twins are continuously updated with real-world data, allowing you to simulate the impact of different operational scenarios and maintenance strategies in a risk-free environment.
- Test the effect of changing operating parameters on asset health.
- Simulate "what-if" scenarios for different maintenance interventions.
- Accelerate operator training and troubleshooting.
Edge AI for On-Premise Deployment
For applications requiring low latency, data privacy, or operation in remote environments, we deploy AI models directly onto edge devices near your machinery. This enables real-time decision-making without relying on constant cloud connectivity.
- Reduce data transmission costs and network bandwidth requirements.
- Ensure continuous operation even during network outages.
- Process sensitive data on-site to meet strict security and compliance mandates.
Model Performance Monitoring & Retraining
An AI model is not a "set it and forget it" solution. We implement robust MLOps practices to continuously monitor model performance, detect concept drift, and automate the retraining process to ensure your predictions remain accurate over time.
- Track model accuracy, precision, and recall in real-time.
- Automatically trigger retraining when new data or failure modes appear.
- Ensure your predictive maintenance system evolves and improves with your operations.
Maintenance Workflow Automation
We use AI and RPA to automate routine tasks within your maintenance workflow. From prioritizing work orders to ordering spare parts and scheduling technicians, we help you build a more efficient, intelligent maintenance operation.
- Automatically prioritize maintenance tasks based on risk and urgency.
- Trigger automated parts procurement when a failure is predicted.
- Optimize technician scheduling based on skill, location, and availability.
Data Readiness Assessment
Unsure if your data is ready for AI? We start with a comprehensive assessment of your data sources, quality, and infrastructure. We provide a clear report on your current state and a practical plan to bridge any gaps for a successful PdM implementation.
- Evaluate the quality and completeness of your historical and sensor data.
- Identify the most valuable data sources for predicting failures.
- Provide recommendations for data collection and sensor strategy.
Team Training & Knowledge Transfer
We believe in empowerment. Our engagement includes comprehensive training for your maintenance teams, engineers, and analysts, ensuring they understand how to interpret AI insights and leverage the new system effectively for long-term success.
- Train your team on how to use the new dashboards and tools.
- Foster a data-driven culture within your maintenance organization.
- Provide clear documentation and ongoing support to ensure user adoption.
Our Proven Path to Predictive Power
We follow a structured, four-phase methodology that de-risks your AI investment and ensures a solution that is technically sound, operationally integrated, and aligned with your business goals.
1. Discover & Strategize
We immerse ourselves in your operations, conducting workshops, assessing data readiness, and defining the business case and ROI metrics to build a strategic roadmap.
2. Develop & Validate
Our data scientists and engineers build and train custom AI models, validating their performance against historical data and establishing predictive accuracy benchmarks.
3. Deploy & Integrate
We deploy the validated models into your live environment, integrating them with your CMMS/ERP systems and developing intuitive dashboards for real-time monitoring.
4. Optimize & Scale
We continuously monitor model performance, retrain as needed, and work with you to scale the solution from the initial pilot to across your entire enterprise, driving continuous value.
Empowering Asset-Intensive Industries
Our AI predictive maintenance solutions are tailored to the unique challenges and high-stakes environments of various industrial sectors.
Manufacturing
Maximize OEE by preventing assembly line stoppages, robotic arm failures, and CNC machine breakdowns.
Energy & Utilities
Ensure grid stability and reduce outages by predicting failures in turbines, transformers, and pumps.
Oil & Gas
Enhance safety and prevent costly downtime by monitoring the health of drilling equipment, pipelines, and refineries.
Transportation & Logistics
Optimize fleet availability and reduce service disruptions by predicting failures in engines, transmissions, and critical components.
Aerospace & Defense
Improve mission readiness and reduce maintenance costs by forecasting component wear and failure in aircraft and military vehicles.
Automotive
Increase production throughput by ensuring the reliability of stamping presses, welding robots, and paint shop equipment.
Real-World Results, Measurable Impact
Don't just take our word for it. See how we've helped leading industrial companies turn maintenance into a strategic advantage.
Reducing Assembly Line Downtime for an Automotive Parts Supplier
Industry:
Automotive Manufacturing
Client Overview:
A Tier-1 automotive supplier was facing intense pressure to meet just-in-time delivery schedules. Their primary challenge was frequent, unplanned downtime on a critical CNC machining line, leading to production bottlenecks, missed deadlines, and financial penalties.
Client Testimonial:
"Errna's predictive maintenance solution has been a game-changer. We've moved from constantly fighting fires to proactively managing our asset health. The reduction in downtime has directly translated to higher profitability and better relationships with our OEM customers." - Michael Harper, Plant Manager
Problem:
The company's maintenance strategy was purely reactive. They relied on operator reports or catastrophic failures to trigger maintenance, resulting in an average of 20 hours of unplanned downtime per month on their most critical production line.
Key Challenges:
- Inability to predict failures in critical components like spindle bearings and ball screws.
- Lack of a centralized system for analyzing sensor data from the CNC machines.
- Maintenance teams were overstretched dealing with constant emergencies.
- High costs associated with expedited shipping and overtime to catch up on production delays.
Our Solution:
Errna deployed a comprehensive AI predictive maintenance solution focused on the client's most critical CNC machines.
- Integrated real-time data from vibration, temperature, and acoustic sensors.
- Developed custom machine learning models to detect unique failure patterns for spindle degradation and ball screw wear.
- Provided a dashboard that gave maintenance teams a 2-4 week advance warning of impending failures.
- Integrated alerts directly into their existing work order management system.
Predicting Gearbox Failure for a Wind Farm Operator
Industry:
Renewable Energy
Client Overview:
A leading renewable energy company operates a large wind farm in a remote location. Gearbox failures in their turbines were a major operational and financial challenge due to the high cost of crane rentals, replacement parts, and lost energy production during downtime.
Client Testimonial:
"The ability to predict gearbox failures months in advance has fundamentally changed our O&M strategy. Errna's solution gives us the lead time we need to plan complex repairs efficiently, saving us millions in reactive costs and lost revenue." - Sophia Dalton, Director of Operations
Problem:
Traditional condition monitoring based on 3-month vibration analysis intervals was failing to catch developing faults in time. Catastrophic gearbox failures were occurring with little to no warning, leading to extensive secondary damage and downtime averaging 3-4 weeks per incident.
Key Challenges:
- Difficulty in accessing turbines for frequent manual inspections.
- High cost and logistical complexity of unplanned major component repairs.
- Massive revenue loss during periods of non-operation.
- Data from SCADA systems was not being effectively utilized for predictive purposes.
Our Solution:
Errna implemented an AI solution that leveraged existing SCADA data, supplemented with high-frequency vibration sensor data.
- Built a data pipeline to unify SCADA, oil analysis, and vibration data.
- Developed a hybrid AI model combining physics-based rules with machine learning to predict gearbox and main bearing failures.
- The model provided a Remaining Useful Life (RUL) estimate with a 3-6 month prediction window.
- Created alerts that specified the likely failure mode (e.g., gear tooth pitting, bearing wear), guiding the maintenance plan.
Optimizing Fleet Maintenance for a National Logistics Company
Industry:
Transportation & Logistics
Client Overview:
A national logistics provider with a fleet of over 2,000 heavy-duty trucks was struggling with vehicle breakdowns on the road. These failures caused significant delivery delays, customer dissatisfaction, and high costs for towing and emergency repairs.
Client Testimonial:
"We now fix problems before they happen on the road. Errna's predictive models on our telematics data allow us to bring trucks in for service at the optimal time, increasing fleet availability and dramatically improving our on-time delivery metric." - Brandon Marshall, VP of Fleet Operations
Problem:
The company's preventive maintenance was based solely on mileage, which didn't account for variations in driving behavior, routes, or load weights. This one-size-fits-all approach led to both premature parts replacement and unexpected breakdowns.
Key Challenges:
- High incidence of costly and disruptive on-road breakdowns.
- Inability to leverage the vast amount of telematics data being collected from the fleet.
- Maintenance schedules were inefficient and not tailored to individual vehicle health.
- Negative impact on customer satisfaction due to delivery delays.
Our Solution:
Errna developed an AI platform that ingested and analyzed real-time telematics data from the entire fleet.
- Integrated data from engine sensors, GPS, fuel systems, and fault codes.
- Built machine learning models to predict failures in critical systems like engines, transmissions, and braking systems.
- Created a "Vehicle Health Score" for each truck, allowing fleet managers to prioritize which vehicles needed attention.
- The solution recommended specific maintenance actions and integrated with their dispatch and maintenance scheduling software.
Our Technology & Tools
We leverage a modern, robust technology stack to build and deploy high-performance predictive maintenance solutions.
What Our Clients Say
We're proud to be a trusted partner in our clients' digital transformation journeys. Here's what they have to say about our impact.
The level of technical expertise and industry knowledge at Errna is second to none. They didn't just provide a tool; they delivered a strategic solution that has fundamentally improved our operational reliability.
The integration with our existing SAP PM system was flawless. The automated work order generation has saved our planners countless hours and eliminated human error. It's a truly connected system.
We were concerned about the complexity of an AI project, but Errna's team made the process transparent and manageable. Their phased approach allowed us to see value quickly and build confidence for a full-scale rollout.
The real-time dashboards are fantastic. Our entire team, from operators to senior management, now has a clear, consistent view of asset health. It has fostered a proactive, data-driven culture.
Unlike other vendors who pushed a black-box solution, Errna worked with us as a true partner. They took the time to understand our unique equipment and processes, and the custom models they built reflect that deep understanding.
The ROI was clear within the first six months. The savings from preventing just two major failures more than paid for the entire project. This isn't an expense; it's an investment with a significant return.
Meet Our Predictive Maintenance Experts
Our team combines deep expertise in data science, AI/ML engineering, and industrial operations to deliver solutions that work in the real world.
Vishal N.
Senior Data Scientist (AI/ML), specializing in time-series forecasting and anomaly detection for industrial IoT data.
Prachi D.
Certified Cloud & IoT Solutions Expert, architecting scalable data pipelines and edge-to-cloud infrastructure for PdM.
Akeel Q.
Certified AI & Machine Learning Specialist, focused on deploying and optimizing ML models for real-time performance.
Girish S.
Microsoft Certified Solutions Architect, expert in integrating AI insights with enterprise systems like Dynamics 365 and SAP.
Flexible Engagement Models to Fit Your Needs
We offer a range of engagement models to provide the right level of support, expertise, and collaboration for your organization.
Project-Based
Ideal for well-defined initiatives like a pilot project or a data readiness assessment. We deliver a specific outcome on a fixed timeline and budget.
- Clear scope and deliverables
- Perfect for initial proof-of-concept
- Predictable investment
Dedicated Team
Your own dedicated, cross-functional team of data scientists, engineers, and project managers who act as a seamless extension of your in-house capabilities.
- Deep integration and knowledge retention
- Agile and adaptable to evolving needs
- Ideal for long-term, complex programs
AI Center of Excellence (CoE)
We help you build and scale your own internal predictive maintenance capabilities, providing strategic guidance, training, and expert support to foster long-term, self-sustaining success.
- Build lasting in-house expertise
- Establish best practices and governance
- Drive enterprise-wide adoption
Frequently Asked Questions
Ideally, you need two types of data: 1) Sensor data from your equipment (e.g., vibration, temperature, pressure, RPMs) and 2) Historical maintenance records (e.g., failure dates, repair actions). However, even if your data isn't perfect, we can often get started. Our Data Readiness Assessment is designed to evaluate what you have and build a strategy to fill any gaps.
While a full enterprise rollout can be a longer journey, we design our pilot projects to deliver measurable value within 4-6 months. By focusing on a single, high-impact asset or production line, we can quickly demonstrate the ROI and build the business case for broader implementation.
Absolutely. That's our core value proposition. We act as your extended AI and data science team. We handle all the complex technical aspects, from data engineering to model building and deployment, allowing your team to focus on what they do best: maintenance and operations.
Traditional condition-based monitoring (CBM) often relies on simple, fixed thresholds (e.g., "alert if vibration exceeds X"). AI-powered predictive maintenance is far more sophisticated. It learns complex patterns across multiple sensors, understands the normal operating envelope, and can predict failures weeks or months in advance, long before a simple threshold is breached. It moves from "what is happening now" to "what is going to happen."
Yes. We offer flexible deployment options, including cloud, on-premise, and hybrid models. For applications with strict data security requirements or in locations with limited connectivity, we can deploy our AI models on edge devices or local servers, ensuring your data never leaves your facility.
Ready to Eliminate Unplanned Downtime?
Let's talk about your specific challenges and how a custom AI solution can transform your maintenance operations. Schedule a complimentary, no-obligation consultation with one of our predictive maintenance experts today.