Libera Global AI
  • Introduction
  • The Problem: Complex Landscape
  • The Libera Large Vision Model
    • Key Features
    • Data Flow
    • Value Flow
  • Positioning
    • Data Criteria
    • Data Crowdsourcing
  • Integration of AI Agents across Value Chain
  • Token Utility: The Role of $LIBE
  • Inclusive Ecosystem
  • Benefits for Data Buyers
  • Sustainability and Real-World Impact
  • A Future of Data-Driven Retail
  • Team
  • Peer into the Future of Retail Innovation with Libera’s LVM
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  1. Positioning

Data Crowdsourcing

PreviousData CriteriaNextIntegration of AI Agents across Value Chain

Last updated 1 month ago

To ensure diverse and representative training data while enhancing engagement and value flow within the ecosystem, Libera empowers users to contribute through our Consumer app by:

  • Capturing product images to create unique, high-quality datasets.

  • Participating in gamified challenges that make data contribution fun and rewarding.

  • Engaging in motivating quests designed to encourage consistent participation.

  • Earning rewards that drive long-term user retention and ecosystem growth.

Merchants are also incentivized to regularly share shelf images, allowing to:

  • Maintain compliance with brand and retailer agreements.

  • Reduce reliance on external agencies for store audits and compliance checks.

  • Optimize trade marketing budgets, which can account for at least 20% of total revenues.

  • Improve inventory visibility and ensure products are always stocked efficiently.

By integrating visual data from Libera's merchant and consumer apps, this multimodal system ensures an ethical and comprehensive data framework. Users are rewarded for their contributions, fostering a continuous and sustainable data supply.