> For the complete documentation index, see [llms.txt](https://docs.batching.ai/batchingai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.batching.ai/batchingai/tech-architecture/tech-infrastructure.md). # Tech Infrastructure ### BACK-END INFRASTRUCTURE Batching.ai operates on a robust back-end infrastructure to support efficient AI image generation and smooth gaming environments. Particularly, the game's back-end is built on a server architecture designed to efficiently handle game data, user interactions, and real-time gameplay. This includes databases to store user profiles, game records, and card collections. ### AI INTEGRATION The utilization of AI technology in Batching.ai primarily focuses on AI image generation. These AI algorithms analyze the visual and descriptive elements of NFTs to generate attributes for the respective NFT cards. Batching.ai's AI integration technology assists in:\ ① Generating new card NFTs when users create them through their owned NFTs.\ ② Ensuring the aesthetic value of existing NFTs.\ ③ Creating NFT cards that maintain appropriate game balance. --- # 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 dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.batching.ai/batchingai/tech-architecture/tech-infrastructure.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.