Every AI-for-carbon connector on the market ships with the same footnote: your data must first be clean, centralised and structured. We…

D-Carbonize

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Every AI-for-carbon connector on the market ships with the same footnote: your data must first be clean, centralised and structured. We built ours without it. Because clean, centralised, structured data is the output of a carbon project, not its input. Ask any industrial group where its consumption data actually lives: a PDF from the utility, a scanned fleet invoice, an ERP export nobody reconciles, a spreadsheet one site manager maintains alone. The D-Carb MCP starts there. On the mess. You drop in your energy invoices and you type, in Copilot, Gemini, Claude or ChatGPT, whichever assistant you are using: "Encode these energy invoices." → It reads the documents as they are. No template, no pre-formatting, no import file → It searches the right emission factors → It writes the measures straight into the Carbon Cockpit → Every line stays traceable, factor by factor, source by source On our industrial accounts, the data collection cycle now runs 2 to 3 times faster. That number matters for one reason only. A Sustainability team has a fixed number of hours. Every hour not spent chasing data is an hour spent on the reduction plan. Data collection was never the point. Reduction is. We said the main course was coming. Here it is. 👉 Book a demo, we'll run it on your own invoices (link in comments)