Energy
AI for grid operations and energy assets that optimises generation, predicts failures, and keeps you compliant
You get AI agents for grid optimisation, renewable generation forecasting, predictive asset management, and regulatory compliance. They run on your cloud, connect to your SCADA and market systems, and produce outputs your control room and regulators can trust.
Built on your AWS, Azure or GCP tenant. NERC CIP aligned. Regulator-ready.
Proof of concept
To measurable ROI
AWS, Azure or GCP
The grid is getting harder to manage. Renewables, distributed generation, and ageing assets demand AI.
Renewable penetration is growing faster than your forecasting models can handle. Grid complexity increases with every new solar farm, battery storage unit, and EV charging point. Meanwhile, your existing assets are ageing, your workforce is thinning, and regulators expect you to maintain reliability while accelerating decarbonisation.
We build AI agents that forecast renewable generation, optimise grid operations, predict asset failures, and automate compliance reporting with the governance and audit trails your regulators and control room operators require.
reduction in grid balancing costs through AI-powered renewable forecasting (McKinsey, 2024)
global energy AI market value projected by 2030 (BCG X, 2024)
reduction in unplanned outages with AI predictive asset management (McKinsey QuantumBlack)
improvement in renewable generation forecast accuracy achievable with ML models (Deloitte Insights)
From SCADA signal to grid decision, in one workflow
We connect your SCADA systems, IoT sensors, weather feeds, market platforms, and asset management tools to AI agents that optimise dispatch, forecast generation, predict equipment failure, and produce the regulatory filings your compliance team requires.
SCADA, IoT, market feeds and asset records
- Approval gates
- Human-in-loop
- Audit logging
AWS, Azure or GCP
- Grid forecasts
- Asset health reports
- Compliance filings
What we deliver for energy companies
From grid optimisation to asset management, governed, auditable, enterprise-ready
Grid optimisation and load balancing
Optimise dispatch and cut grid balancing costs in real time.
ROI
Process
Ingest grid data
SCADA readings, demand forecasts, generation schedules, and market prices streamed in real time from grid systems
Optimise dispatch
AI models calculate optimal generation dispatch, demand response activation, and storage charge/discharge schedules
Deliver recommendations
Control room operators receive actionable recommendations with projected cost and reliability impact for each option
Sources
Based on published findings: McKinsey QuantumBlack, 'AI in Grid Operations', 2024; BCG X, 'Intelligent Grid Management' — reproduced for illustrative purposes
Renewable generation forecasting
Forecast wind and solar output with 40% higher accuracy.
ROI
Process
Integrate weather and asset data
Numerical weather predictions, satellite imagery, turbine/panel telemetry, and historical generation data fused
Generate probabilistic forecasts
ML models produce generation forecasts at multiple time horizons with confidence intervals and uncertainty quantification
Feed trading and operations
Forecasts delivered to energy trading desks and grid operations teams via API or control system integration
Sources
Based on published findings: McKinsey, 'AI and the Energy Transition', 2024; Deloitte Insights, 'Renewable Forecasting with AI' — reproduced for illustrative purposes
Predictive asset management
Predict equipment failures before they cause outages.
ROI
Process
Monitor asset health
Vibration, temperature, oil analysis, partial discharge, and operational data from critical assets streamed continuously
Predict failures
ML models identify degradation patterns and forecast remaining useful life for each monitored asset
Schedule maintenance
Risk-ranked maintenance recommendations integrated with your work management and outage planning systems
Sources
Based on published findings: McKinsey QuantumBlack, 'Predictive Maintenance in Utilities', 2024; BCG X, 'AI for Asset Performance' — reproduced for illustrative purposes
Energy trading and market analytics
Optimise trading positions across day-ahead and intraday markets.
ROI
Process
Aggregate market data
Power prices, gas prices, carbon prices, weather forecasts, and interconnector flows consolidated from all market sources
Model and optimise
AI generates optimal trading strategies across markets, accounting for your generation assets and contract positions
Execute and track
Trading recommendations with P&L projections delivered to traders, with post-trade analysis and model recalibration
Sources
Based on published findings: BCG X, 'AI in Energy Trading', 2024; McKinsey, 'Digital Energy Trading' — reproduced for illustrative purposes
Demand forecasting and response
Forecast demand at feeder level and activate response programmes.
ROI
Process
Collect demand signals
Smart meter data, weather forecasts, economic indicators, and industrial load profiles aggregated across your service area
Forecast demand
ML models produce granular demand forecasts at the feeder, substation, and system level across multiple time horizons
Activate demand response
AI identifies optimal demand response events and dispatches signals to enrolled customers and aggregators
Sources
Based on published findings: Deloitte Insights, 'Smart Grid and Demand Response', 2024; McKinsey, 'AI-Powered Demand Management' — reproduced for illustrative purposes
Solar and storage optimisation
Optimise solar and battery dispatch for cost and revenue.
ROI
Process
Monitor generation and storage
Solar inverter data, battery state of charge, tariff schedules, and market signals collected from distributed assets
Optimise dispatch
AI calculates optimal charge/discharge schedules that balance self-consumption, grid export, and flexibility market revenue
Execute and report
Dispatch instructions sent to battery management systems with revenue attribution and performance reports
Sources
Based on published findings: BCG X, 'Distributed Energy and AI', 2024; McKinsey, 'Behind-the-Meter Optimisation' — reproduced for illustrative purposes
Regulatory compliance and ESG reporting
Auto-generate regulatory returns and ESG disclosures.
ROI
Process
Map regulatory obligations
Regulatory requirements across jurisdictions mapped to your operational data sources and reporting schedules
Extract and validate
Generation, emissions, reliability, and financial data extracted, cross-referenced, and validated against regulatory definitions
Produce filings
Regulatory returns, emissions reports, and ESG disclosures generated in required formats with full audit trails
Sources
Based on published findings: Deloitte Insights, 'Energy Regulatory Technology', 2024; McKinsey, 'ESG Reporting in Utilities' — reproduced for illustrative purposes
CEO, PG&E
Patti Poppe
"We are using AI on Google Cloud to analyse satellite imagery, weather data, and grid sensor readings to predict wildfire risk across our service territory. The models identify high-risk circuits before conditions become dangerous, and we are making targeted safety shutoffs that protect communities while minimising unnecessary outages."
Originally published: Google Cloud Customer Stories — PG&E — reproduced for illustrative purposes
How we work in energy and utilities environments
Energy AI operates in safety-critical, regulated environments. We start by mapping your SCADA systems, asset management platforms, and market interfaces, then identify the one use case where AI will create the most immediate, measurable impact on reliability, cost, or compliance.
Everything we build runs on your cloud tenant. Your SCADA data, your asset records, your market positions. None of it leaves your environment. Your control room, CISO, and regulators can audit the system directly.
01
Data and use case mapping
We assess your SCADA, asset management, and market systems, identify the highest-value AI use case, and map it to your regulatory and safety obligations.
02
30-day proof of concept
A working AI agent on your cloud, connected to your operational data, with demonstrable output in 30 days.
03
Governance by design
NERC CIP alignment, audit trails, and explainable outputs built in from the start. Structured for regulator review and control room trust.
04
Scale in 12-18 weeks
From one validated operational workflow to measurable reliability, cost, and compliance improvements across your generation, transmission, or distribution operations.
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