Methane deadlines arrive in 2027 for the EU. Energy compliance teams have months, not years, to prove they are monitoring emissions across every site they operate.

The deadline is one version of a problem energy operators already know well. (Carbon Capture) CCS sites need conformance monitoring. Wind portfolios need consent and endangered species habitat evidence. Each of these can be reduced to the same pattern of data operation: join satellite or continuous measurement in  imagery to your asset portfolio geometries, at scale, on a schedule. Desktop tools cannot keep pace, and general data platforms were not built for raster processing. The result is missed windows, manual analyst hours, and blind spots between passes.

This session will provide an overview and demo of the stack that changes the economics.

  • WherobotsDB runs raster and vector together at planetary scale in standard Spatial SQL, flagging methane plumes and flare events, then attributing each one to a specific asset.
  • RasterFlow adds ML inference when a workload needs a trained model, like detecting unmapped oil and gas infrastructure across high-resolution imagery.
  • The Wherobots MCP server puts all of it behind plain language, so an AI agent drives the stack directly, exploring catalogs, designing queries, and orchestrating pipelines without manual authoring.

We will run it live. Watch an agent take a plain-language question, find every flare across our sites over the past year and attribute it to an asset, and return a source-attributed answer in minutes.