AI for
Energy & Utilities
Grids built for power flowing one way now have to balance rooftop solar, EV charging and weather patterns the old models weren't built for. We build the forecasting, asset analytics and dispatch systems that help keep supply steady.
The real problems
What strains the grid.
Grid load forecasting
Day-ahead forecasts built on older statistical models miss the swings that solar, EVs and heatwaves now cause. Procurement pays for every miss.
Renewable intermittency
Solar and wind output moves with the weather. Without a granular forecast, balancing means paying for expensive reserves and curtailing generation you didn't need to lose.
Field workforce scheduling
Crews still run on fixed schedules while outages and new connection requests pile up. Travel time eats into the hours they could spend on the job.
Regulatory reporting
CEA and CERC filings assembled by hand from meter, SCADA and finance systems, every single cycle. Slow, repetitive, and easy to get wrong.
Our solutions
AI for the modern grid.
Load forecasting models
Short and medium-term forecasts that learn from weather, calendar and consumption patterns down at feeder level, not just the system as a whole.
Asset-health analytics for substations
Transformer and switchgear condition scored from DGA, thermal and loading data, so replacement budgets follow real risk instead of asset age alone.
Smart-meter data platforms
Ingestion and analytics for large volumes of meter reads: loss detection, theft flags and consumption segmentation.
Outage prediction & crew dispatch AI
Storm and equipment-failure predictions paired with crew planning, so the right trucks are staged before the outage, not after it.
How we engage
Grid-safe delivery.
Data & network audit
Meter coverage, SCADA points and historian quality mapped feeder by feeder before any modelling starts.
Pilot on one region
One distribution region or substation cluster, with forecasts and health scores checked against a full season of actual data.
Validate & expand
Accuracy reviewed with your planning and procurement teams. Rollout expands region by region once the numbers hold up.
Sustain & retrain
Seasonal retraining, drift monitoring and dashboards that your load-dispatch centre owns and runs day to day.
Common questions
Energy & utilities AI, answered directly.
Can load forecasting handle solar and EV-driven demand swings?
Yes - short and medium-term forecasts learn from weather, calendar and consumption patterns down to feeder level, built specifically to catch the swings older statistical models miss from solar, EVs and heatwaves.
How do you score substation asset health - is it just based on age?
No - transformer and switchgear condition is scored from DGA, thermal and loading data, so replacement budgets follow real risk instead of asset age alone.
Can you help with CEA/CERC regulatory filings?
Yes - regulatory reporting that's normally assembled by hand from meter, SCADA and finance systems each cycle can be automated, cutting the repetitive work and the error risk that comes with manual assembly.
What can a smart-meter data platform actually catch that we're missing today?
Loss detection, theft flags and consumption segmentation across large volumes of meter reads - patterns that are essentially invisible without a platform built to process meter data at that scale.
How does outage prediction improve on how we currently dispatch crews?
Storm and equipment-failure predictions are paired with crew planning, so trucks get staged before an outage happens instead of dispatched after, cutting response time and reducing miles driven on emergency calls.
How do you pilot this without risking grid reliability?
We start with a data and network audit mapped feeder by feeder, then pilot on one distribution region or substation cluster, checking forecasts and health scores against a full season of actual data before expanding region by region.
Start the conversation
Let's benchmark your forecast.
Book a free discovery session with our energy team. Bring us your current forecast numbers, and we'll show you what a machine-learning baseline could look like against them.