Getting more technical from here — this is where the platform actually runs the building.
BMS
Engineering, control logic and automation.
An interactive schematic editor and node-based automation builder, with pre-built control components for boilers, heat pumps, AHUs and lighting groups, plus bulk device import.
Day-ahead pricing, sub-metering and battery dispatch.
Day-ahead price optimisation, whole-portfolio metering and PV/battery-aware control — energy management that’s typically guaranteed to save at least 10%.
Live dashboards for every zone, asset and meter, automatic alerts routed to the right person, and a full logbook — the same layout whether you watch one building or fifty.
Automatic load shifting and battery/PV dispatch against the live ENTSO-E day-ahead market, plus predictive peak shaving before costly demand peaks happen.
Every space, asset and I/O point lives in one live, clickable model — the single source of truth for design and operation. The same twin scales from a single building to a full portfolio, with one login across every site.
Run Climatics as a managed cloud service, fully on-premise, or a hybrid of both — your policy decides. White-label deployment is available if you want to run it under your own brand.
Bring sensors, meters and gateways online in minutes, not a bespoke integration project. Wireless and wired devices land in the same digital twin as your BMS points — no separate silo.
Encrypted transport, authenticated access and network isolation, built to a high security standard. Every control change is logged — who, what, when — queryable from the logbook.
A documented Open REST API plus a real-time WebSocket feed — the exact same API Climatics itself is built on, with no private, faster internal shortcut.
Single sign-on and role-based access for every person who touches the platform, with per-site permissions for contractors and partners, and a full activity log.
Neural Twins, demand forecasting, a room simulator, PID auto-tuning and a weather-impact model — all trained on your own operational data, learning the specific fingerprint of your buildings.
Large-language-model reasoning layered over the operational models, in plain language — ask what tomorrow’s weather or price curve means for a specific building.
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