> For the complete documentation index, see [llms.txt](https://academy.shade.inc/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://academy.shade.inc/help-center/general-faq/our-storage-and-subprocessors.md). # Our storage and subprocessors See our subprocessors and storage providers We currently store data using our subprocessors Cloudflare R2 (for Active drives) and Wasabi (for Vault drives). Both are vetted, S3-compatible storage providers that follow the most stringent of security protocols (including AES-256 encryption at rest and SOC 2 Type II policies). Your data is transported and secured with HTTPS and no one on the Shade team ever sees your data. For more information on additional subprocessors please check out our [trust center](https://trust.shade.inc). To power AI features, some of your content is also processed by the following subprocessors: * **Google Cloud (Gemini on Vertex AI)** - AI metadata, tagging, and analysis of images, video and documents * **AssemblyAI** - Transcription of audio and video Your data is also never used to train any of our AI (see our [Data Policy](https://shade.inc/data-usage-policy-and-ai)). Finally, metadata such as tags, EXIF, and and AI embeddings are stored in our Postgres Database hosted by Google Cloud. --- # 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://academy.shade.inc/help-center/general-faq/our-storage-and-subprocessors.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.