Geospatial, Climate, Energy & Industrial AI Service Providers

This RFI evaluates providers that design, build, deploy, and operate AI-enabled solutions using spatial, earth-observation, climate, energy-system, operational-technology (OT), engineering, and industrial data. For this study: - Geospatial & Earth Intelligence AI includes GIS, satellite/airborne/drone imagery, SAR, LiDAR, point clouds, maps, positioning, spatial analytics, and location-aware decisioning. - Climate & Environmental AI includes weather and climate intelligence, physical climate risk, environmental monitoring, emissions measurement/management, nature and biodiversity analytics, and climate adaptation or resilience planning. Transition-risk, carbon accounting, ESG reporting, and sustainability data management are in scope when AI is material to the solution. - Energy AI includes power generation, renewables, grids and utilities, oil and gas, energy trading/forecasting where linked to physical energy systems, storage, EV charging, energy efficiency, and decarbonisation. It may use geospatial, climate, asset, market, engineering, and OT/IT data. - Industrial AI includes manufacturing, process industries, mining, construction, logistics/transport assets, and other physical operations; examples include asset performance, reliability, quality, safety, process optimisation, digital twins, robotics, and connected-worker solutions. AI is not limited to conventional ML. Respondents should include relevant production use of generative AI, multimodal foundation models, agentic workflows, and physical AI/robotics. Do not claim these capabilities where they are only experimental, generic, or not material to client value.

Status: active