Logo
  • Cases & Projects
  • Developers
  • Contact
Sign InSign Up

© Copyright 2025 Many.Dev. All Rights Reserved.

Product
  • Cases & Projects
  • Developers
About
  • Contact
Legal
  • Terms of Service
  • Privacy Policy
  • Cookie Policy
Development of an AI-Powered Product Recognition App for Sustainable Retail
  1. case
  2. Development of an AI-Powered Product Recognition App for Sustainable Retail

This Case Shows Specific Expertise. Find the Companies with the Skills Your Project Demands!

You're viewing one of tens of thousands of real cases compiled on Many.dev. Each case demonstrates specific, tangible expertise.

But how do you find the company that possesses the exact skills and experience needed for your project? Forget generic filters!

Our unique AI system allows you to describe your project in your own words and instantly get a list of companies that have already successfully applied that precise expertise in similar projects.

Create a free account to unlock powerful AI-powered search and connect with companies whose expertise directly matches your project's requirements.

Development of an AI-Powered Product Recognition App for Sustainable Retail

3sidedcube.com
Consumer products & services
Environmental Services
Retail

Challenge of Plastic Pollution and Packaging Reduction in Retail

Retailers face increasing pressure to eliminate plastic packaging while maintaining customer access to product information. Traditional packaging creates waste, harms marine ecosystems, and conflicts with sustainability goals. Businesses require technology-driven solutions to deliver product details without physical labels in package-free retail environments.

About the Client

A cosmetics brand committed to ethical practices and environmental sustainability, pioneering packaging-free product innovations

Objectives for Sustainable Retail Solution

  • Create a technology solution enabling package-free product sales with digital information delivery
  • Enhance customer experience through instant product recognition and interactive education
  • Support global sustainability goals by eliminating single-use plastic packaging
  • Establish scalable framework for machine learning-based product identification

Core System Functionalities

  • Real-time product recognition using image recognition
  • Instant display of product details (ingredients, benefits, origins)
  • Multi-language support for global accessibility
  • Integration with in-store devices for seamless user experience
  • Machine learning model for continuous product database expansion

Technology Stack Requirements

TensorFlow Lite (Google's ML framework)
Mobile application development (iOS/Android)
Cloud-based image database
Edge computing for low-latency recognition

System Integration Needs

  • Lush product database synchronization
  • In-store device management systems
  • Customer analytics platform integration

Performance and Scalability Criteria

  • Sub-200ms recognition response time
  • 99.9% uptime guarantee for in-store operations
  • Support for 1000+ concurrent users
  • Data privacy compliance (GDPR, CCPA)
  • Scalable architecture for global store deployment

Expected Environmental and Business Impact

Projected reduction of 1.3 million plastic bottles annually per store location, with potential for 10x scalability across global retail networks. Enhanced customer engagement through interactive product education, with measurable improvements in sustainability metrics and brand loyalty. Technology framework enables future applications in supply chain transparency and waste reduction initiatives.

More from this Company

Development of a Blood Donation Mobile Application to Enhance Donor Engagement and Streamline Blood Supply Management
Development of Offline-Capable Mobile App and Admin Portal for Remote Healthcare Support in Sub-Saharan Africa
Development of a Gamified Circular Economy Platform for the Hospitality Sector
Development of a Community-Focused Platform for Independent Businesses
Development of an Assisted Crossing Mobile Application for Enhanced Pedestrian Accessibility