> For the complete documentation index, see [llms.txt](https://knowledge.aterio.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://knowledge.aterio.io/data-products/data-centers/use-cases.md). # Use Cases #### **IT Infrastructure Planning** Optimize data center placement, capacity, and timing using Aterio’s detailed project-level data—including development stage, power estimates, and square footage. Site selection and capacity planning are informed by real-time insights into where hyperscale and colocation facilities are expanding across the U.S. **Energy Consumption Analysis** Leverage Aterio’s modeled power demand by site and operator to analyze regional energy needs, load profiles, and sustainability gaps. The dataset supports utilities, grid planners, and ESG teams in understanding the operational footprint of current and future data center clusters. **Market Analysis** Assess competitive density and future supply by analyzing Aterio’s inventory of announced, under-construction, and active data centers. Filter by provider, location, or megawatt scale to identify underserved markets, regional saturation, or strategic partnership opportunities. **Policy Analysis** Support state and federal energy, land use, and infrastructure policy by tracking where data center growth is concentrated and how much power it will require. Aterio’s dataset enables proactive planning around energy resilience, water usage, and emissions associated with digital infrastructure. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://knowledge.aterio.io/data-products/data-centers/use-cases.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.