Unplanned downtime is one of the most expensive challenges in Energy operations. This solution enables organizations to move from reactive to predictive maintenance by unifying operational data and applying advanced analytics and machine learning.
Frame delivers the solution through data engineering, analytics, and AI platform implementation services that support reliable, real-time insights and scalable deployment across critical assets.
Operators face costly unplanned downtime due to fragmented telemetry, inconsistent sensor quality, and a maintenance model that reacts to failures rather than predicting them. These gaps limit visibility into asset health and increase operational risk.
Sensor data scattered across SCADA, PLCs, historians, and field devices.
Inconsistent or noisy telemetry preventing accurate trend analysis.
Reactive maintenance cycles based on time intervals instead of equipment condition.
High downtime costs and safety exposure due to late detection of failures.
Limited ability to understand degradation patterns across critical assets.
Frame builds a predictive maintenance platform that unifies operational telemetry into a scalable time-series lakehouse and applies machine learning to detect anomalies and predict failures. The solution enables reliability teams to shift from reactive to proactive asset management.
Unified OT data ingestion into a centralized Databricks time-series lakehouse.
Automated signal processing pipelines for vibration, temperature, pressure, and motor current.
ML-driven anomaly detection, fault classification, and Remaining Useful Life (RUL) predictions.
Asset-specific feature engineering tailored to compressors, ESPs, turbines, and rotating equipment.
Integrated alerts and recommended actions in SAP PM, Maximo, or maintenance dispatch workflows.
Organizations that adopt predictive maintenance gain earlier visibility into degradation trends, reduce downtime, optimize maintenance planning, and improve asset reliability. The shift to predictive operations drives measurable savings and enhances safety.
20–40% reduction in unplanned downtime through early detection of failures.
Extended asset life via condition-based maintenance and optimized repair timing.
Reduced maintenance spend from fewer field visits and minimized emergency repairs.
Fleet-wide visibility into asset health across rigs, plants, and operational regions.
A scalable reliability foundation enabling additional AI and automation use cases
Our Approach – Minimize risks and maximize return.
Frame delivers with confidence through proven, repeatable methods that reduce cost overruns, project delays, and operational uncertainty. Our AI-enhanced delivery model helps to ensure consistent execution across cloud-native, analytics-driven architectures—while hyper-local teams and nearshore scale drive efficiency without compromising quality. Frame’s prebuilt accelerators, reusable IP, and tight feedback loops fast-track speed-to-value while maintaining full transparency and shared accountability every step of the way.
Discover & Design
Start with business goals, identify the right opportunities, define the value and chart a clear plan.
Build & Modernize
Extend and modernize your data and architecture with scalable platforms and secure pipelines while complimenting your existing systems.
Scale & Evolve
Scale high-impact AI use cases, establish sustainable governance, and evolve with frameworks that support agility, adoption, and long-term value.
Value Delivery
We don’t just modernize—we deliver outcomes you can measure: faster cycles, smarter operations, and durable solutions that evolve with change.
