> For the complete documentation index, see [llms.txt](https://docs.atlas.design/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.atlas.design/atlas-ai-studio-overview.md). # Atlas AI Studio Overview Most AI tools look incredible in demos. Then you try them in production and spend more time fixing outputs than you saved generating them. Atlas is different. Atlas is AI-native production infrastructure for 3D content. It orchestrates 100+ AI models into game-ready pipelines for professional studios, sitting as the application layer between foundational AI models and shipping production work. Available on Google Cloud Marketplace. ## What Atlas does Atlas pairs a node-based visual workflow editor with a multi-agent AI assistant. Artists and engineers compose production workflows from generation, optimization, and post-processing models without writing integration code, then run those workflows from inside Unreal Engine, Unity, Blender, or via direct API. The platform spans the full 3D production pipeline: image generation, image editing, image-to-3D conversion, mesh optimization, retopology, UV unwrapping, PBR texturing, multi-view conditioning, video, audio, and animation. Each step is a node; nodes connect into reproducible, versioned, exportable workflows. ## Who Atlas is for AAA and AA game studios that need to produce 3D content at scale without sacrificing production quality. Atlas is built for the customer that pays significantly more per asset than individual creators: studios with art directors, technical art teams, and engine programmers shipping real games on real deadlines. Atlas customers include Square Enix, Parallel, and other AAA studios across Japan, Korea, Europe, and the United States. ## How to use this documentation This guide explains how Atlas works, what each node does, and how to build production workflows. Start with Getting Started for a 10-minute orientation. Use Node Index for reference on every available capability. The User Manual covers operating the platform end to end. Atlas x GCP covers the Google Cloud deployment story. The Ask feature on any page lets you query the docs directly. Append `?ask=` to any URL and the server returns a focused answer with citations. Designed for AI agents and busy humans equally. {% content-ref url="/pages/e6nB40Vt4vaQCg3prgSW" %} [Getting Started](/atlas-ai-studio-overview/getting-started.md) {% endcontent-ref %} {% content-ref url="" %} {% endcontent-ref %} {% content-ref url="/pages/bZW3OYUQix6hx1nTgPWw" %} [3D & Gaming Focus](/atlas-ai-studio-overview/3d-and-gaming-focus.md) {% endcontent-ref %} {% content-ref url="/pages/WGxg5VROWJfkG7Hen0RT" %} [Atlas x GCP](/atlas-ai-studio-overview/atlas-x-gcp.md) {% endcontent-ref %} {% content-ref url="/pages/nDq2E7XRFOeHp5U1wHuk" %} [Testimonials](/atlas-ai-studio-overview/testimonials.md) {% endcontent-ref %} {% content-ref url="/pages/AXuWTHBqwv42iiXkm8So" %} [User Manual](/atlas-ai-studio-overview/user-manual.md) {% endcontent-ref %} {% content-ref url="/pages/BTNe9YP7AV3m1QACbxBi" %} [Node Index](/atlas-ai-studio-overview/node-index.md) {% endcontent-ref %} --- # 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.atlas.design/atlas-ai-studio-overview.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.