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Power Efficiency. Predict Reliability. Sustain the Future.

We enable predictive maintenance, grid optimization, and energy analytics for power producers and utilities. From renewable forecasting to smart energy distribution, our AI systems ensure every kilowatt delivers maximum impact.

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Before AI v/s After AI

KPI COUNTERS – THE SCOREBOARD

Unplanned Downtime / Year ↓ 45 %
Before AI: 210 Hours
After AI: 115 Hours
Energy Efficiency (kWh Saved / Month) ↑ 33 %
Before AI: 68 %
After AI: 90 %
Unplanned Outage Duration
Before Light After Light Before Dark After Dark
Energy Waste (Line Loss%)
Before RFQ (Light) After RFQ (Light) Before RFQ (Dark) After RFQ (Dark)
Asset Health Prediction Accuracy
Before RFQ (Light) After oee (Light) Before RFQ (Dark) After oee (Dark)

Why Change?

Pain-Point Matrix

Unplanned Equipment Failures
Predictive maintenance models identify failures before they occur.
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Unplanned Equipment Failures
AI correlates vibration, temperature, and current data to flag degradation weeks in advance.
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Energy Wastage & Inefficiency
Optimization algorithms reduce losses across generation and transmission.
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Energy Wastage & Inefficiency
Dynamic load balancing adjusts voltage and flow for maximum output efficiency.
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Demand-Supply Imbalance
AI forecasts demand peaks using real-time and historical consumption data.
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Demand-Supply Imbalance
Machine learning adapts to weather and behavioral patterns to preempt overload scenarios.
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Renewable Intermittency
Forecasting models stabilize solar and wind integration into the grid.
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Renewable Intermittency
AI learns production volatility and dynamically reallocates reserve capacity.
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Regulatory & ESG Compliance
Automated reporting ensures full traceability of carbon and energy metrics.
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Regulatory & ESG Compliance
AI compiles compliance-ready ESG dashboards validated to ISO and GRI standards.
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High Downtime in Maintenance
Predictive scheduling minimizes manual inspections and idle time.
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High Downtime in Maintenance
AI prioritizes maintenance based on asset risk probability and real-time condition.
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Data Fragmentation
Unified AI layer integrates SCADA, IoT, and ERP systems seamlessly.
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Data Fragmentation
Cross-platform connectors enable one-click access to multi-source operational data.
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Aging Infrastructure Risks
Digital twins simulate asset behavior and degradation scenarios.
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Aging Infrastructure Risks
AI-driven digital replicas predict performance loss under stress conditions.
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Manual Energy Audits
Computer vision automates the inspection of meters, valves, and panels.
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Manual Energy Audits
OCR and vision models digitize and verify physical readings with audit trails.
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See the data fly

Workflow Spotlight

Production Lane Icon
Predictive Maintenance Lane

Step 1

Sensor Data Capture

SCADA and IoT devices collect vibration, temperature, and current signals.

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Commercial Lane Icon
Energy Optimization Lane

Step 1

Real-Time Load Monitoring

Smart meters track energy consumption across transformers and substations.

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AI Solutions

Energy Efficiency Optimization

Key points

  • Load optimization reduces power wastage in transmission and distribution.
  • AI models detect abnormal power draw or line loss instantly.

Smart Grid Analytics

Key points

  • Forecasts demand and supply variations in real-time.
  • Balances load across transformers and substations automatically.

Sustainability & ESG Tracking

Key points

  • Monitors energy usage, waste, and emission data continuously.
  • Generates auto-compliance reports for ESG and ISO standards.

Predictive Maintenance

Key points

  • AI predicts component failure using vibration, temperature, and current signals.
  • Maintenance schedules automatically adjust to prevent unplanned downtime.

Renewable Integration Intelligence

Key points

  • Models renewable variability for stable hybrid grids.
  • Predicts solar and wind generation capacity.

Asset Performance Management (APM)

Key points

  • Detects equipment anomalies.
  • Simulates future performance with AI twins.

Energy Demand Forecasting

Key points

  • Learns seasonal and weather-based patterns.
  • Predicts consumption across zones, cities, or grids.

Visual Safety Analytics

Key points

  • Detects faults in pipelines, cables, and panels using AI vision.
  • Automates thermal imaging to spot overheating.

Automated Reporting

Key points

  • Collects, validates, and formats energy and audit data.
  • Reduces report preparation time by 70%.

Under the hood

Technical Architecture

  • Smart gateways connect distributed assets securely.
  • On-prem edge inference for latency-critical monitoring.
  • Interoperable with Modbus, DNP3, and OPC-UA protocols.

  • Predictive modeling for asset health and grid performance.
  • Time-series learning for energy forecasting.
  • NLP-based compliance documentation generation.

  • Hybrid deployment with AWS IoT Core and Azure Energy Cloud.
  • Scalable lakehouse model via Snowflake integration.
  • REST and MQTT APIs for ERP, CRM, and BMS integration.

  • Anomaly detection for grid and asset security threats.
  • AI-driven intrusion detection across control networks.
  • Encrypted edge-to-cloud communication for data protection.

  • Ingests live feeds from sensors, SCADA, and energy meters.
  • Kafka + Delta architecture ensures real-time processing and consistency.
  • Latency <150ms for high-frequency data ingestion.

  • Continuous training pipelines for forecasting models.
  • Version-controlled deployments with MLflow and Databricks.
  • Built-in explainability for regulatory transparency.

  • Live dashboards for power efficiency and downtime analytics.
  • 3D equipment and grid visualization for operational clarity.
  • Custom energy flow maps for management reports.

  • Real-time carbon monitoring.
  • Predictive emissions modeling.
  • AI-optimized energy routing for net-zero initiatives.

Implementation Blueprint

Phase 1: Energy System Assessment

  • Audit asset health, grid structure, and IoT readiness.
  • Map data pipelines from SCADA, sensors, and meters.
  • Identify AI opportunities for efficiency and uptime.

Phase 2: Model Training & Pilot Setup

  • Train predictive models using historical maintenance and energy data.
  • Deploy initial POC for asset failure and load forecasting.
  • Validate model accuracy and operational performance.

Phase 3: System Integration

  • Connect AI engines with ERP, SCADA, and BMS system.
  • Build unified dashboards for grid visibility and insights.
  • Implement alerting and control automation modules.

Phase 4: Workforce Training & Optimization

  • Train engineers and operators on AI dashboards and alerts.
  • Conduct scenario-based sessions for predictive interventions.
  • Optimize workflows for minimal manual dependency.

Phase 5: Continuous Improvement Cycle

  • Schedule model retraining for evolving grid patterns.
  • Automate ESG reporting and sustainability metrics.
  • Expand AI coverage to new plants and facilities.

ROI Calculator

Yield Gain:

Downtime Savings:

Energy Savings:

Payback:

Trust & Compliance Badges

G20 Light G20 Dark
Ministry of Finance, Government of India Ministry of Finance, Government of India
Ministry of Tourism, Government of India Ministry of Tourism, Government of India
Government of New South Wales | Investment NSW Government of New South Wales | Investment NSW
Sulzer Sulzer
The Cosmos Co-Operative Bank Limited The Cosmos Co-Operative Bank Limited

The Fast Lane

Accelerate your energy transition with modular AI accelerators from predictive maintenance to grid intelligence. Go from pilot to full-scale deployment in just 8 weeks, achieving measurable energy savings and sustainability goals.

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Why Choose Us

Proven Results,
Trusted by Experts

Futuristic Visuals

Real-time load flows, asset health overlays, and predictive alerts visualized with precision for operators overseeing complex energy networks.

Trusted by Industry Leaders

Our solutions are deployed by power utilities, renewable energy firms, and grid operators backed by industry-grade compliance and cybersecurity frameworks.

Loss Aversion

Without AI:

Downtime increases Energy waste grows Maintenance remains reactive

We help you prevent failures and drive sustainability goals forward.

Small Steps, Big Impact

Book a short strategy session to uncover how predictive maintenance and analytics can reduce downtime and improve energy efficiency.