Your smart meters generate millions of readings. Your grid planning still uses last quarter averages.

Energy and utility companies face a data problem that compounds at every layer: massive IoT sensor volumes, strict regulatory reporting obligations, and aging infrastructure that was never designed for real-time analytics. Smart meters generate interval data at scale. SCADA (supervisory control and data acquisition) systems capture grid operations continuously. Asset management systems track equipment across decades-long lifecycles. And every quarter, federal and state regulators require reports assembled from all of these sources.

Most energy companies have invested in sensors, meters, and monitoring. The data flows into historians and databases. What does not exist is the layer that connects that raw data to the operational dashboards, predictive models, and compliance reports the business needs. CorrDyn builds exactly that layer.

Real-time sensor and telemetry pipelines. Streaming data pipelines that ingest sensor data at millisecond intervals and store it for time series analysis. Whether the source is SCADA historians, smart meter interval data, or distributed generation telemetry, the architecture handles billions of readings per day and scales as your grid modernization program expands.

Operational dashboards for grid and asset management. Real-time operational dashboards that show generation output, distribution losses, equipment utilization, outage status, and demand patterns. Operations teams get sub-second data freshness and the reliability they need to depend on the numbers every shift.

Telemetry cost optimization. Energy companies running large-scale time series analytics often face runaway compute costs as data volumes grow. We profile actual query patterns, storage costs, and resource utilization, then eliminate waste without reducing analytical capability. At one enterprise client, we reduced telemetry platform compute costs by 30%.

Regulatory reporting automation. NERC (North American Electric Reliability Corporation) filings, FERC (Federal Energy Regulatory Commission) market reports, state public utility commission submissions, and environmental compliance data all require pulling from operations, finance, and engineering systems. We build the pipelines that automate this assembly, replacing manual processes that consume senior staff time under deadline pressure every cycle.

ML infrastructure for predictive analytics. For energy companies ready for predictive maintenance, load forecasting, or anomaly detection, we build the data preparation layer that makes machine learning viable: clean time series data, feature engineering pipelines, and model deployment infrastructure. The same time series ML patterns we have deployed in industrial manufacturing apply directly to grid equipment monitoring and generation forecasting.

We built our IoT and time series expertise in industrial environments where the data volumes, reliability requirements, and operational stakes match what energy companies face. Billions of sensor readings per day. Dashboards that operations teams depend on every shift. Data quality requirements where errors have real consequences. We bring this depth to energy and utilities without the learning curve of a team encountering industrial-scale data for the first time.