> For the complete documentation index, see [llms.txt](https://docs.otari.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.otari.ai/privacy/data-collection.md). # Data Collection Otari collects and processes data necessary to operate, secure, and improve its services. This includes account information, usage data, and metadata associated with API requests. ## **Account Information** We collect information such as your name, email address, and account details to manage your account, provide support, and communicate service-related updates. ## **Location & Interaction Data** We collect IP addresses, approximate location information, website and service usage data, browser and device details, and referral information. We may also use cookies and third-party analytics tools to monitor interactions, improve our services, and support legal and regulatory compliance. ## **Usage Data & Metadata** Otari tracks API request metadata, including token consumption, model and provider usage, latency, and costs. This information supports usage analytics, monitoring, billing, and troubleshooting. ## **AI Requests** Prompts and responses are processed to route requests to the selected AI providers. If agent session content is enabled, Otari may also store prompts, tool-related information, and data evaluated by guardrail checks, and, where your workspace has turned it on, model responses. Otari also stores a model response when you ask it to: a response to a request sent with an idempotency key is kept for a limited period so the request can be safely retried, and a Playground conversation you choose to save keeps its replies until you delete it. Data handling and retention policies may vary depending on the AI provider and your workspace configuration. For more information about how we collect, process, store, and protect your data, please refer to our [Privacy Policy](https://otari.ai/privacy). --- # 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://docs.otari.ai/privacy/data-collection.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.