> For the complete documentation index, see [llms.txt](https://docs.ta-da.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ta-da.io/ta-da-platform/architecture/production.md). # Production ### **User Journey** Users can vizualize jobs available to them on their dashboard. Some jobs may not be available to all users, based on their profile. There are 4 types of jobs available: * Data collection: Voice, Photo and Videos * Surveys: answer questionnaires * Social media: visit a social media page and engage * Project review: visit a website or download an app and complete actions A job can consist of several tasks, which must all be completed before the user can validate their production. Jobs can either be "unique", which means a user can only complete the job once. Or they can be "multiple" which means a user can submit the same job several times. ### **UX highlights** Ta-da's production interface has been built around key principles, to balance scalability and user-experience with * deeply customizable tasks, ensuring guidelines can be adjusted to each campaign * Adaptive UX based on user behavior and performance. * Support user-specific contents, based on past behaviour, performance or profile * Light frameworks and responsive UIs to maximize accessibility across continents #### ### **Back-end highlights** To optimize throughput and reduce latency, our back-end is designed to support: * **Smart routing**: ensuring the right user gets the right task based on skill, availability, or past performance. * **Concurrency handling**: when millions of microtasks are being processed in parallel, race conditions, double submissions, or stale task serving can degrade performance. * **Fraud detection models** (e.g., device fingerprinting, behavioral analysis), * **KYC-lite mechanisms** for sensitive data and **trust scoring** systems for long-term integrity. * Robust **metadata pipelines**, **version control for datasets**, to maintain links between raw input, annotations, and audit logs. #### --- # 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.ta-da.io/ta-da-platform/architecture/production.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.