> For the complete documentation index, see [llms.txt](https://kenna-ai.gitbook.io/kenna-ai-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://kenna-ai.gitbook.io/kenna-ai-docs/getting-started/what-is-kenna-ai.md). # What is Kenna AI? Kenna AI is an all-in-one AI design studio built for product designers, fashion designers, and creative teams. Unlike standalone AI image generators, Kenna gives you 20+ specialized tools that work together in a connected workflow. The core idea is simple: every tool's output becomes the next tool's input. You start with a sketch, render it into a photorealistic product, apply a pattern to the product, create colorways, put it on a virtual model, and relight the scene — all without leaving the studio or re-uploading files. Kenna is designed for professionals who need production-ready product imagery. Every tool is purpose-built for product visualization, from packaging mockups and asset extractions, to virtual try-on. You can access Kenna at studio.kenna.ai from any modern web browser. No software to install. --- # 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://kenna-ai.gitbook.io/kenna-ai-docs/getting-started/what-is-kenna-ai.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.