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A web platform that connects businesses with human labelers and data providers to accelerate the commercialization of labeled datasets. Built with Flask, HTML, CSS, and JavaScript.

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VeldAI | The Data Labeling & Commercialization Platform

VeldAI is a fictional yet functional platform built using Flask, HTML, CSS, and JavaScript. It aims to connect data providers, labelers, and companies seeking annotated datasets. Whether you're looking to profit from unused data, earn money labeling data, or access high-quality labeled datasets for AI development — VeldAI bridges those needs.

Live Preview: https://veldai-first-try.onrender.com/


Project Overview

VeldAI simulates a modern data marketplace and labeling platform:

  • For Clients: Upload raw data to get it labeled, or browse ready-to-use datasets.
  • For Labelers: Join the platform and earn money by tagging and annotating data.
  • For Data Owners: Monetize your image, text, or video datasets securely.

Tech Stack

  • Frontend: HTML, CSS, JavaScript
  • Backend: Python, Flask
  • Deployment: Render

Features

  • Interactive landing page
  • Navigation through roles: Labeler, Data Provider, Client
  • Data-type explanations (Images, Text, Video)
  • CTA sections for joining or submitting data
  • Social links and contact areas
  • Fully styled UI using custom CSS with Google Fonts and Font Awesome
  • Labeler interface (in development)

Getting Started

  1. Clone the repo
    git clone https://github.com/Arts-HCS/veldai-data-platform.git
    cd veldai-data-platform
    
  2. Create and activate a virtual environment
    python3 -m venv venv
    source venv/bin/activate
    
  3. Install dependencies
    pip install -r requirements.txt
    
  4. Run the Flask app
    flask run
    
  5. Open your browser and go to: http://127.0.0.1:5000/

Still in Development

  • Client dashboard

  • Dataset submission form

  • Admin panel

  • Payments and analytics mockups


Preview

Screenshot 2025-07-29 at 23 25 48

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A web platform that connects businesses with human labelers and data providers to accelerate the commercialization of labeled datasets. Built with Flask, HTML, CSS, and JavaScript.

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