Anticipate, Prevent and optimize with AI-Powered predictive maintenance

Keep Your Digital Systems Resilient with Proactive, AI Powered Predictive Maintenance

Predictive Maintenance leverages advanced analytics and AI to unify monitoring across assets, applications, and infrastructure layers. By correlating telemetry, logs, and alerts in real time, we enable proactive detection of hardware failures and software anomalies before they disrupt critical services.

This data-driven approach empowers organizations to reduce recurring incidents, optimize maintenance plans, and ensure higher uptime, keeping systems reliable, resilient, and business-ready.

Use Case | Telco

With a vast portfolio of OSS/BSS applications supporting millions of daily transactions, the telecom operator required a proactive approach to ensure service continuity and maintain strict SLA commitments.

The challenge:

Frequent spikes in response times and hidden error patterns created a risk of unexpected application downtime. Manual log analysis made it difficult to predict issues early, often leading to reactive fixes and potential SLA breaches. The goal was to leverage operational data to anticipate failures, protect customer experience, and reduce unplanned service interruptions.

Our approach:

Log & Metrics Correlation
Integrated logs, response times, and error rates into a unified monitoring layer.
Predictive Analysis
Applied advanced anomaly detection to forecast potential outages before they impacted services.
Proactive Alerts
Enabled teams to act early with real-time notifications of performance degradation across OSS/BSS systems.

The results:

Reduced Unplanned Downtime
 
Early detection of anomalies minimized service interruptions and SLA penalties.
Enhanced Customer Experience

Proactive issue management ensured consistent application performance for end-users.

Operational Agility
Streamlined monitoring and predictive alerts allowed faster response and more efficient resource allocation.

Use Case | Energy

Operating multiple solar parks with hundreds of inverters, the company aimed to maximize energy production while minimizing unexpected equipment failures and costly field interventions.

The challenge:

Unplanned inverter failures disrupted energy output and required expensive emergency maintenance. Relying on manual checks and historical data limited the ability to anticipate issues before they occurred. The goal was to use real-time telemetry and environmental data to predict failures, boost yield, and reduce operational costs.

Our approach:

Telemetry Integration
Collected inverter voltage, current, and ambient data into a centralized analytics platform.
Predictive Modeling
Applied machine learning to detect early signs of underperformance or potential failure.
Actionable Insights
Delivered maintenance recommendations to prioritize interventions before critical breakdowns.

The results:

Higher Energy Yield
 
Early detection of performance degradation ensured consistent power generation.
Lower Maintenance Costs

Predictive insights reduced emergency interventions and optimized scheduled maintenance.

Improved Asset Lifespan
Proactive care extended inverter durability, protecting long-term infrastructure investment.

Use Case | Energy

Managing extensive oil field operations with critical pumping and valve systems, the company sought to improve reliability and extend equipment lifespan while reducing unplanned downtime.

The challenge:

Pump and valve failures led to costly production halts and emergency repairs. Traditional maintenance schedules based on fixed intervals failed to detect early signs of wear, causing avoidable breakdowns. The goal was to leverage real-time operational data to predict component degradation, minimize downtime, and optimize asset lifecycle management.

Our approach:

Operational Data Monitoring
Collected and analyzed flow rates, pressure changes, and vibration signals from field equipment.
Failure Prediction Models
Applied advanced analytics to detect patterns indicating early-stage wear in pumps and valves.
Maintenance Prioritization
Delivered actionable insights to schedule interventions before critical failures occurred.

The results:

Reduced Unplanned Downtime
 
Early detection of wear prevented breakdowns and production stoppages.
Extended Equipment Lifespan

Data-driven maintenance improved the durability of pumps and valves.

Lower Operational Costs
Optimized maintenance planning cut emergency repair expenses and resource waste.

Use Case | Telco

Supporting critical backend systems such as billing, charging, and inventory, the telecom operator needed to ensure database performance to maintain seamless service delivery and meet high transaction demands.

The challenge:

Growing data volumes and complex queries were causing index bloat, locking conflicts, and performance degradation across core systems. Issues were often detected only after impacting operations, leading to reactive fixes and service delays. The goal was to anticipate database bottlenecks, enable proactive tuning, and ensure consistent performance under heavy loads.

Our approach:

Query Metrics Analysis
Collected detailed execution metrics to detect slow queries and locking trends.
Index Health Monitoring
Implemented predictive checks to identify and address index bloat before it degraded performance.
Proactive Scaling
Delivered insights to optimize database configurations and scale resources ahead of demand.

The results:

Consistent System Performance
 
Proactive tuning reduced slowdowns across billing, charging, and inventory platforms.
Faster Issue Resolution

Early detection of query and index problems enabled fixes before impacting operations.

Improved Scalability
Data-driven insights supported smooth scaling as transaction volumes increased.

Benefits

Unified health dashboard

Real-time view of asset, app, infra, and service health in one place.

Proactive issue detection

Predict hardware failures and software anomalies before they impact users.

Data driven maintenance plans

Combine telemetry, logs, alerts and history to guide interventions.

Higher uptime and business continuity

Less disruption, more confidence in infrastructure and application layers.

Challenges

Siloed monitoring tools

Siloed monitoring tools

Infrastructure, application and network health are not analyzed together, delaying issue detection.

Recurring incidents with no pattern detection

Recurring incidents with no pattern detection

Frequent software errors or job failures go unresolved without root cause prediction.

Ineffective alert threshold

Ineffective alert threshold

Static thresholds miss dynamic anomalies in software performance.

Unplanned system downtime

Unplanned system downtime

Both hardware and software failures disrupt business-critical services.

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Contacts

PT: +351 213 174 421
UK: +44 203 771 7300
US: +1 571-314-0931
BE: +32 2 203 87 41
IN: +91 88843 34234

Contact Us

Sponsored

© 2025 InnoWave. All rights reserved

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