Energy& Utilities
Asset failures and compliance gaps are predictable when your operational data is connected and current.
We build the data infrastructure connecting your SCADA, CMMS, ERP, and GIS systems, giving asset managers, operations teams, and regulators a single source of truth for performance and risk.
Monitor asset reliability, maintenance cost per unit, and crew performance across distributed infrastructure, with models that predict failure before it occurs.
The operational reality of energy and utilities
Asset failures that data could have predicted
Equipment failure data, sensor readings, and maintenance history all exist in separate systems. The data to predict failures sits unused while the failures happen.
Outage data disconnected from work management
Outage records live in one system. Work orders and crew dispatch live in another. Connecting them for analysis requires manual effort that rarely happens.
Capacity planning is reactive
Load forecasting and generation capacity planning run on models that update quarterly. The grid changes faster than the planning cycle can track.
Regulatory reporting consumes massive analyst time
Regulatory filings require data from multiple operational systems that must be manually compiled, reconciled, and formatted. The process repeats every cycle.
The KPIs we surface for energy and utility operators
How we help energy and utility companies
Asset performance management dashboards
Real-time asset health, reliability metrics, and maintenance status across the full asset portfolio. SAIDI, SAIFI, and CAIDI tracked by circuit, substation, and region.
Predictive maintenance models on equipment data
Equipment failure prediction built on sensor data, inspection records, and maintenance history. Know which assets need attention before they fail.
Outage prediction and response analytics
Outage risk models that identify vulnerable circuits and equipment ahead of weather events. Response analytics that optimize crew dispatch during active outages.
Load and generation forecasting
Short and medium-term load forecasting models. Generation capacity planning analytics for mixed portfolios including renewables.
Regulatory reporting automation
Automated extraction and formatting of regulatory reporting data. Reduce the analyst hours spent compiling compliance filings from multiple operational systems.
Computer vision for asset condition inspection
Drone and fixed camera imagery analyzed by computer vision models for asset condition scoring. Replace manual inspection cycles with continuous condition monitoring.
Some of the systems we connect with
These are some of the platforms we connect. We integrate with almost any system. If you run it, we can likely work with it.
Where this applies
Distribution utility: predictive maintenance on transformer fleet
A distribution utility experienced recurring transformer failures that caused extended outages. Maintenance was calendar-based and did not account for actual asset condition or load stress.
We built a predictive maintenance model on load data, thermal readings, and maintenance records. The utility now prioritizes maintenance based on asset health scores. Transformer failure rate decreased by 28% in the first year.
Electric utility: SAIDI/SAIFI tracked by circuit in real time
A regional electric utility tracked SAIDI and SAIFI at the company level monthly. Circuit-level reliability was not visible until the annual regulatory report, which was too late to act on.
We connected the outage management system and asset registry to a real-time reliability dashboard. Circuit-level SAIDI and SAIFI are now visible daily. Operations can target maintenance and vegetation management at the circuits driving the most outage minutes.
Renewable energy operator: generation forecasting accuracy
A wind and solar portfolio operator used vendor-provided generation forecasts that performed poorly in the operator's specific geographic and weather conditions.
We built custom generation forecasting models calibrated on 3 years of historical performance data. Forecast accuracy improved by 22% on a mean absolute percentage error basis, reducing dispatch imbalance costs significantly.
Outcomes you can expect
Related Services
Frequently asked questions
Predict failures before they cost you.
Book a free call and we will assess your asset data maturity and map a path to predictive maintenance.