SEP 22, 2026
WSP iTwin AI Platform
UTILIZED SOFTWARE
iTwin, OpenBridge, OpenRoads, ProjectWise
LOCATION
India
ORGANIZATION/COMPANY
WSP Global
Infrastructure teams hold extraordinary sustainability intelligence inside their digital models, yet most of it stays locked away. On the WSP iTwin AI Platform project in Bangalore, fragmented tool ecosystems meant that anyone without specialist BIM expertise, licenses, and time for manual extraction was effectively shut out of the insights that drive greener design decisions. Cost estimation, carbon analysis, and quality checking each lived in isolated applications, so the questions that matter most — how much carbon is in this design, and where is it coming from — were slow and expensive to answer.
WSP changed that by connecting the Bentley iTwin Platform to a conversational AI layer, translating plain-English questions into precise model queries through a simple web interface. Seventeen production capability tabs now span design, construction, and operations, putting model intelligence in the hands of engineers, planners, and decision-makers alike — no specialist licenses required.
The sustainability payoff is immediate. Data extraction time fell by 75%, turning work measured in days into work measured in seconds. A full embodied carbon report of 16.6 tCO2e was generated in under fifteen seconds alongside an 8.7 million USD cost estimate, and instant carbon screening pinpointed structural fill as the dominant driver at 92.2% of total emissions — the kind of insight that lets teams redesign while change is still cheap. Compliance validation, clash detection, and model quality audits that once consumed days now run automatically.
Beyond carbon, the platform strengthens resilient infrastructure more broadly: it is discipline-agnostic across water and road assets, and monitors structural hazards through live sensor alerts, flagging conditions such as a 26.3 mm settlement event before they escalate. Already deployed on the Tel-Aviv M2 Metro and Mountain View Corridor projects, it shows how making model data conversational makes sustainability decisions faster, broader, and better informed.
