> 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/our-modeling-approach/data-quality.md).

# Data Quality

Aterio follows a structured validation process to ensure that key figures such as total power capacity, number of facilities by state, or provider-specific footprints are accurate and aligned with industry disclosures. Each data point is verified using a combination of public sources, including **utility filings**, **10-Q reports from public companies**, **permitting records**, and **satellite imagery**.

For power capacity, we confirm generator counts and models through permitting data and visual inspection. These estimates are then cross-checked against manufacturer specifications and official provider disclosures. For example, if *Microsoft* reports using 3MW Caterpillar C175-16 generators, we validate both the type and number installed to ensure our totals reflect reality.

At the regional level, we reconcile our data center counts with regulatory filings, operator announcements, and public infrastructure documents. When a provider claims a specific footprint in a state, we locate and verify each facility individually to ensure that our numbers are both granular and consistent with what the industry reports.

<br>


---

# 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/our-modeling-approach/data-quality.md?ask=<question>&goal=<user_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.
