Your models are only as good as the energy data they can trust.
EDX turns messy device telemetry into one structured, quality-scored data layer for training, reporting, anomaly detection, and production inference, so your analytics team spends less time fixing inputs and more time building value.
The layer between raw hardware telemetry and the datasets your analysts or ML pipelines actually want to work with.
Energy data pipelines usually break long before the model does.
Most analytics teams do not struggle with modeling first. They struggle with inconsistent schemas, silent telemetry failures, and extraction work that keeps rebuilding the same cleaning logic around vendor APIs that were never meant for data products.
A cleaner path from device telemetry to usable datasets.
Bring solar, battery, EV charging, meter, and project telemetry into one access layer.
Map different device types into one shared object model your pipelines can rely on.
Attach freshness, completeness, and connection health before the data reaches training or reporting.
Move from historical extraction to dashboards, anomaly detection, and live production scoring on the same base.
Designed for teams that need energy data to be analysis-ready, not just available.
Need better inputs for analytics or AI?
We’ll walk through data health signals, extraction patterns, schema design, and what it takes to support both model training and live operational analytics from the same foundation.