Think of this as a smart energy coach for buildings and homes: it studies how people actually use appliances (lights, HVAC, devices) and then an AI assistant explains, in plain language, what to change and when to use things to cut energy waste without making occupants uncomfortable.
Manual energy audits and generic efficiency tips rarely change day‑to‑day behavior in buildings. This framework turns raw usage data into personalized, actionable guidance so occupants and facility managers can run appliances more efficiently and lower energy bills and emissions.
Combining behavioral usage modeling with prescriptive, conversational LLM guidance tightly embedded in the built-environment workflow (smart homes, BMS dashboards) and validated on real appliance usage data can create a data + workflow moat over time.
Hybrid
Vector Search
High (Custom Models/Infra)
Context window cost and latency for fine-grained, per-user guidance at scale, plus storage and processing of continuous appliance-usage time-series data.
Early Adopters
Unlike generic smart-energy tips or rule-based building management, this approach explicitly models occupant behavior and appliance usage patterns, then uses an LLM as a guidance layer to deliver personalized, prescriptive recommendations in natural language across appliances and contexts.