Pi will enable teams to test combinations of actions, targets, and assumptions — and see their implications across carbon, social, and economic dimensions. Users will be able to model outcomes such as emissions reduction, cost efficiency, or local value creation using both internal data and verified public reference values.

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This bridges qualitative reasoning and quantitative validation, while recognising that not all sustainability trade-offs can or should be reduced to a single number. For broad transition plans (“Where do we invest €10M to maximise impact?”) Pi supports structured, multi-criteria exploration; for focused decisions (“Rainwater harvesting or greywater reuse?”) it drives toward quantified comparables with qualitative benefits captured alongside.

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Underlying this is a semi-structured data store — a living scratchpad that captures the numbers and parameters that matter to each team. It learns as people work, reuses known values transparently, and reduces repetitive data requests, while avoiding the heavy lift of ERP or carbon-system integrations.