Evervia continuously learns from real-time energy, weather, and grid signals to adjust how organisations consume energy — reducing cost, inefficiency, and operational waste without manual intervention.
Energy consumption, weather patterns, pricing signals, grid conditions
Models usage behaviour, detects inefficiencies, forecasts near-term demand
Determines when, where, and how energy should be used across operations
Recommendations, alerts, workflow triggers, and future device-level control
Most systems show energy data. Evervia turns that data into decisions that actively change consumption behaviour.
Energy intelligence for organisations that cannot afford dedicated energy management teams or expensive enterprise tooling.
Every optimisation decision reduces avoidable waste while supporting efficiency and net-zero operational goals.
Data: Elexon BMRS API + Open-Meteo · Gradient Boosting model · Updates every 30 mins
GridSense is a live UK energy forecasting tool that ingests Elexon BMRS grid data and Open-Meteo weather signals, runs a trained Gradient Boosting model, and surfaces demand predictions and efficiency insights — available via tiered API access.
What changes
Organisations already have more data than they can act on. Evervia sits between consumption and operations as a decision layer, learning how energy is used and adapting patterns to reduce waste.
Understand historical and live energy behaviour across sites, equipment, and operational schedules.
Forecast near-term usage based on weather, occupancy, pricing, and operational conditions.
Shift, reduce, or prioritise energy use based on cost, efficiency, and business constraints.
Trigger workflows, alerts, recommendations, and future control-layer integrations.
Why now
Businesses need systems that actively reduce exposure to unpredictable energy costs — not just alerts that arrive too late to act.
Smart meters, weather APIs, and grid data now make real-time energy intelligence possible at a fraction of the historical cost.
Modern learning systems can convert fragmented consumption data into adaptive operational decisions at SME scale.
We are looking for early SME and public sector partners interested in testing autonomous energy optimisation — systems that learn, decide, and improve over time.
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