Demand forecasting, grid optimization, and predictive maintenance. Infrastructure AI that prevents failure.
We build the systems that predict demand, optimize distribution, and prevent outages.
Energy infrastructure is aging, demand patterns are shifting, and grid complexity is increasing. We build AI systems for demand forecasting, predictive maintenance, grid optimization, and sustainability reporting, for utilities, renewable operators, and energy retailers.
Why AI in Energy is different.
The grid was designed for one-way power flow from centralized plants to consumers. Rooftop solar, battery storage, and EV charging have shattered that model. Distributed energy resources now account for over 30% of new generation capacity in major markets. Utilities that cannot forecast and manage bidirectional flow will face reliability crises.
AI-powered demand forecasting achieves 93-95% accuracy on 15-minute intervals, compared to 80-85% from traditional methods. Predictive maintenance on generation and transmission assets delivers 15-25% O&M savings. Non-technical losses from energy theft cost utilities $96B globally, and ML detection models are the only scalable solution.
Energy AI has a unique constraint: failure is not a business inconvenience, it is a public safety event. Models must be explainable to regulators, resilient to adversarial conditions, and integrated with SCADA systems that predate the internet. That combination of accuracy requirements and legacy integration is what makes this work hard.
The Problems That Slow You Down
Demand forecasting misses by double digits
Over-generation wastes fuel and capital. Under-generation means blackouts or expensive spot purchases. Forecasting accuracy is the difference.
Asset maintenance is calendar-based, not condition-based
Transformers, turbines, and distribution equipment serviced on fixed schedules. Some get maintained too often, some not enough.
Sustainability reporting is a manual quarterly project
ESG data scattered across operations, procurement, and finance. Reporting takes weeks and the numbers are always questioned.
Grid visibility gaps grow with distributed generation
Rooftop solar, battery storage, and EV charging add complexity. The grid was designed for one-way power flow.
Systems for Energy
Demand Forecasting
ML models that predict energy demand by region, time window, and customer segment, factoring in weather, events, and distributed generation.
View service →Predictive Asset Maintenance
Sensor data from transformers, turbines, and grid equipment for failure prediction 2-4 weeks ahead, with maintenance schedules optimized.
View service →Grid Analytics Platform
Real-time visibility across generation, transmission, and distribution, including distributed energy resources.
View service →Sustainability Dashboards
Automated ESG data collection, carbon accounting, and regulatory reporting, continuous, not quarterly.
View service →These aren't pitch deck scenarios. Every use case maps to a system pattern we've built and deployed in production.
What We Build in Energy
Predictive Maintenance for Generation & Transmission
Head of Asset ManagementTime-based maintenance leads to over-servicing or catastrophic failures; millions/day lost to outages.
LSTM/Random Forest on IoT sensors (vibration, temperature, oil) + failure records, producing RUL predictions and explainable dashboards for CMMS.
Renewable Generation Forecasting
VP RenewablesSolar/wind volatility causes curtailment, fossil backup overuse, grid imbalances.
Hybrid physics-ML (transformers/LSTM on weather, satellite, historical gen), delivering 24-48 hour forecasts into SCADA/EMS.
Demand Response & Load Forecasting
Head of Demand ResponsePeak demand strains grid; inaccurate short-term forecasts lead to inefficient DR activation.
Gradient boosting/LSTM on smart meter data, weather, events, producing granular 15-60 min forecasts and automated DR signals.
GridMind Contingency Analysis Agent
Director of System OperationsOperators manually run N-1 simulations for thousands of contingencies; delays risk blackouts.
LLM multi-agent system parses natural-language requests, calls power flow tools, returns prioritized risks with explanations.
Customer Service & Outage Agent
VP Customer ExperienceCall centers handle repetitive billing/outage queries; outage reporting siloed, delayed field response.
LLM agent triages outage reports, checks smart meter/SCADA, provides ETAs, routes complex cases, integrating with billing/CRM.
Energy Theft Detection
Head of Loss PreventionNon-technical losses cost utilities $96B globally; manual checks miss sophisticated patterns.
Isolation forest/graph ML on smart meter data, usage patterns, network topology, producing risk scores for targeted inspections.
Vegetation Management Risk Scoring
VP Distribution OperationsTree growth causes 20-30% of outages; manual surveys costly and infrequent.
CV on LiDAR/drone imagery + ML growth models, producing prioritized trimming routes and clearance violation predictions.
Building the system is half the job. Growing the business around it is the other half.
How We Grow Energy Brands
AEO for Energy Software Queries
Marketing DirectorEnergy SaaS misses 51% of "best grid management software" to ChatGPT; zero-clicks cut RFPs 34%.
Schema case studies/specs, compliance FAQs, Perplexity tracking to demo requests.
LinkedIn Ads for Utility Tools
Demand Gen LeadLinkedIn CPL $95-135 for ops software; under 9% SQL.
Ops exec targeting, ROI calcs, HubSpot nurture.
HubSpot Lead Scoring for Renewables
Marketing Ops Manager1.8K leads/quarter; 67% unqualified.
HubSpot scoring (downloads, visits), sequences (forecasting demos), CRM sync.
How We Work With Energy
Machine Learning Solutions
Prediction systems that run 24/7. Not proof-of-concepts gathering dust.
Learn more →Data Engineering & Analytics
Your data is scattered across 15 tools. We wire it into one system that answers questions.
Learn more →Intelligent Automation
The repetitive judgment calls your team makes 200 times a day? We build systems that handle them.
Learn more →Cloud & Infrastructure
Cloud architecture, migration, Kubernetes, monitoring: the foundation under everything else.
Learn more →Frequently asked questions
Typically 3-5% MAPE (mean absolute percentage error) for day-ahead forecasting, improving to under 2% for week-ahead with sufficient historical data.
Yes. We integrate with OSIsoft PI, GE Predix, ABB, and custom SCADA via OPC-UA, Modbus, or REST APIs.
We aggregate data from smart meters, inverters, and battery management systems into a unified analytics layer. Works with both owned and third-party DERs.
AI moves fast. Stay ahead.
No spam. One actionable email per week on AI systems and growth.
Grid failures are predictable. The question is whether you're predicting them.
Start with an asset and grid data assessment. We'll identify where predictive AI delivers the fastest reliability improvements.