Sub surface ops
Predictive Maintenance
for Oil and Gas Industry
Anticipate equipment failures and maximize uptime with AI‐powered health monitoring tailored for oil & gas operations
Trained on historical failure data
Our Predictive Maintenance platform leverages high-frequency vibration, temperature, pressure and flow data from pumps, compressors, valves and subsea boost pumps to forecast equipment failures before they occur. By applying domain-specific ML models, trained on historical well-pad and platform failure data, we detect subtle patterns indicative of bearing wear, seal degradation and fluid fouling.
- 36%Reduction in unplanned downtime on offshore platform
- 20%Maintenance‐cost savings (≈ $2 Bln/year) across global pump networks
- 15 Million +Daily anomaly predictions for critical rotating equipment
Ready to eliminate unexpected shutdowns and drive reliability across your assets?
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Key modules
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Sensor Data Ingestion
Scalable time-series data lake built for high-throughput SCADA and IIoT feeds -
Anomaly Detection Engine
Oil & gas–tuned ML models flag vibration and pressure anomalies in real time -
Maintenance Scheduler
Automated work-order generation with priority-ranking by asset criticality -
Dashboard & Alerts
Role-based notifications via web, mobile and push for operations and reliability engineers
Integration capabilities
Connects seamlessly with existing systems
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SAP PM
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IBM Maximo
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Oracle EAM
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SCADA
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RESTful APIs
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OPC-UA
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Mobile support
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Offline support
Mission Critical Points Addressed
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Equipment health scoring and KPI tracking (MTBF, MTTR) - Failure-mode root-cause analysis for pump, compressor and valve faults
- Automated preventive work-order creation and spare parts reservation
- Data governance & security: role-based access controls, end-to-end encryption and audit-ready logs
Implementation Roadmap & Timeline
Phase 1 – Pilot
- Onboard 5–10 critical assets on one platform or pump pad
- Integrate sensor feeds and configure anomaly thresholds
- Validate initial model outputs with reliability engineers
Phase 2 – Scale
- Expand to 50–100 assets across multiple sites
- Develop asset-specific ML retraining pipelines
- Integrate with CMMS for automated work-order execution
Phase 3 – Enterprise Rollout
- Full fleet deployment (500+ assets)
- Advanced analytics: remaining-life forecasts, multi‐asset risk heatmaps
- Continuous model refinement and cross-site KPI benchmarking
Request a detailed implementation roadmap and timeline to plan your pilot today
Training and Change Management Support
We provide onsite and virtual workshops for operations, maintenance and IT teams
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Role-based training (reliability engineers, technicians, planners)
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Train-the-trainer programs for continuous in-house expertise
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Dedicated support SLAs and quarterly health-check reviews
KPIs Beyond Downtime & Cost
- 50%
Faster
50% Faster Mean Time to Detect (MTTD) - 30%
Maintenance Planning Efficiency
30% Improvement in scheduled vs. ad-hoc work - 15%
Spares Inventory Reduction
15% Fewer critical spare purchases due to better forecasting
Ready to eliminate unexpected shutdowns and drive reliability across your assets?
Frequently Asked Questions
Our edge-collector buffers and normalizes data, applying interpolation and model-based gap-filling to ensure continuous analysis.
Local inference continues on our edge gateway, and alerts queue until connectivity is restored - ensuring no blind spots.
Thresholds are automatically tuned by our models but can be manually adjusted by reliability engineers via the dashboard.
Local edge buffering holds up to 7 days of data, auto-syncing when connectivity returns.
Most clients observe measurable downtime reduction within the first 3 months of the pilot phase.
Other Solutions
From field to finance, our end-to-end AI suite unlocks uptime, compliance and productivity across your entire oil & gas value chain