Practice
Sustainable Infrastructure Intelligence
Understand the environmental and operational consequences of AI and data-centre infrastructure decisions — energy, carbon, water, location, cooling, workload and hardware — before the capacity is built.
Decisions we model
The questions this practice answers.
- Which market, cooling architecture and energy procurement minimise carbon and water together?
- What is the carbon cost per million tokens, per training run and per unit of useful compute?
- Which changes to procurement, refresh policy or end-of-life pathway reduce emissions fastest?
- How does the design score against the UN Sustainable Development Goals it can evidence?
Products
Sustainable Infrastructure Intelligence products
Sustainable Infrastructure Intelligence
DataCenterCO2
Model energy, carbon, water, location, cooling, workload and hardware choices for AI and data-centre infrastructure.
How we work
Model, compare, optimise, report.
Every engagement follows the same four steps, so assumptions stay visible and comparable from one decision to the next.
Model
Fifteen markets, real accelerator models and lifecycle-aware embodied carbon.
Compare
Baseline against alternative designs, with every change recomputed in full.
Optimise
A ranked list of actions with carbon, water and cost consequences.
Report
Evidence-graded reporting, with PUE, WUE and CUE following ISO/IEC 30134.
Test it, then take it further.
Run the model on your own assumptions first. When the decision needs validated inputs, benchmarks and a management-ready recommendation, bring it to us.