AI LEED Score Optimization
The Problem
“You’re leaving LEED points (and OPEX savings) on the table because building data is siloed”
Organizations face these key challenges:
LEED credit evidence lives across BAS/CMMS/utility portals/spreadsheets—teams spend weeks chasing documents
Energy and IAQ issues are found after bills spike or tenants complain, not when the data first shows drift
Credit strategy is inconsistent across properties; outcomes depend on which engineer/consultant is assigned
Maintenance is reactive—equipment inefficiencies quietly erode performance and jeopardize LEED targets
Impact When Solved
Real-World Use Cases
GPT-4–Enabled Data Mining for Building Energy Management
This is like giving a large commercial building a very smart assistant that can read all its meters, logs, and reports, then explain where energy is being wasted and how to fix it—using natural language instead of dense engineering dashboards.
AI Predictive Maintenance for Commercial Buildings
This is like giving a commercial building a smart “check engine light” that looks at all the sensor data (HVAC, elevators, lighting, water systems) and warns you before something breaks, instead of after tenants complain or systems fail.