Use Cases
Circular IT, Sustainable Compute, and Green AI
Browse grouped use cases within Circular IT, Sustainable Compute, and Green AI.
Use Cases
Implementation scope and rollout planning
Clear next-step recommendation
Track and attribute the carbon emissions of every AI model and service in real-time to meet ESG reporting mandates and identify high-impact reduction opportunities.
Evaluate and score IT hardware and cloud providers on their sustainability metrics to make procurement decisions that align with circular economy goals.
Unify cloud cost management with carbon KPIs to rightsize AI infrastructure, automatically shutting down idle resources and optimizing for both budget and emissions.
Dynamically route AI inference and training workloads to data centers powered by renewable energy, slashing operational carbon footprint without sacrificing performance.
Use machine learning to predict and adjust cooling systems in real-time, cutting PUE (Power Usage Effectiveness) and significantly reducing energy consumption and water usage.
Catalog and compare AI models by their training and inference carbon impact, enabling developers to select the most efficient model for their performance requirements.
Forecast future carbon emissions from AI operations based on planned workloads, allowing for proactive budgeting and strategic planning to stay within sustainability targets.
Automate the tracking, refurbishment, and responsible decommissioning of IT hardware to extend asset life, reduce e-waste, and recover maximum value.
Systematically identify and remove redundant parameters from AI models to create smaller, faster versions that maintain accuracy while drastically cutting compute needs.
Intelligently schedule batch AI training jobs to coincide with periods of high renewable energy generation, ensuring workloads are powered by green electricity.
Deploy IoT sensors and AI analytics to monitor and optimize water consumption for cooling, providing auditable metrics for drought-prone regions and corporate water stewardship goals.
Deploy and manage lightweight AI models at the network edge to minimize data transmission and central cloud processing, reducing latency and overall energy consumption.
Generate audit-ready ESG and regulatory reports by automatically aggregating data on energy, water, and carbon metrics from across your AI infrastructure stack.
Automatically migrate non-critical AI batch jobs across global cloud regions based on real-time carbon intensity data, optimizing for the cleanest available compute.
5+ years building production-grade systems
We look at the workflow, the data, and the tools involved. Then we tell you what is worth building first.
The first call is a practical review of your use case and the right next step.
