White paper
  • WHITE PAPER
    • 📖Introduction: The Data Wall
    • 📖Human Generated Data
    • 📖Scaling data generation with blockchain
  • TA-DA PLATFORM
    • ⚙️Architecture
      • 👷Production
      • ✅Quality Control
      • 💰Rewards
    • ❇️Use-cases
      • 🧠Artificial Intelligence
        • ➡️Audio Datasets
        • ➡️Video Datasets
        • ➡️Image Datasets
        • ➡️Text Datasets
      • 🖇️Data Structuring
    • 🛫Roadmap
  • Token
    • 📊Token Economics
    • 🏦Staking
      • ➡️On-chain Staking
      • ➡️Meria Staking
      • ➡️xExchange Metastaking
  • LINKS
    • 🌎Website
    • 🐦X (Twitter)
    • 🗣️Discord
    • 🗞️Telegram
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On this page
  • User Journey
  • UX highlights
  • Back-end highlights
  1. TA-DA PLATFORM
  2. Architecture

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.

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Last updated 6 days ago

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