Problem Description

The primary objective of this project is to create an AI-powered shopping assistant specifically designed for the Temu service. The assistant should enhance the shopping experience by providing detailed analysis and recommendations based on product reviews within Temu's marketplace. The system will utilize the client's referral code to generate referral income by directing users to high-quality products on Temu.

Very Similar website : shop.app

Key functionalities required:

Review Analysis: The AI assistant should analyze product reviews on Temu to identify and recommend products that are genuinely useful and of high quality.

Referral System Integration: Incorporate the client's referral code into the AI assistant to earn referral income when users make purchases through the app.

User-Friendly Interface: The assistant should have an intuitive and easy-to-use interface, accessible to a broad range of users.

Acceptance Criteria

Effective Review Analysis: The system must accurately analyze and summarize product reviews, highlighting key pros and cons, to aid users in making informed purchasing decisions.

Referral Income Tracking: The assistant must track and report income generated from the referral code, ensuring transparency and accuracy.

Seamless Integration with Temu: The assistant should integrate smoothly with the Temu platform, providing a cohesive user experience.

Multi-Language Support: Considering the global user base, the assistant should support multiple languages.

User Feedback Mechanism: Include a feature for users to provide feedback on the assistant's recommendations and overall functionality.

Privacy and Security Compliance: Ensure all user data is handled securely and in compliance with relevant privacy laws.

Technical Details

AI and Machine Learning: Utilize advanced AI and ML algorithms for natural language processing and sentiment analysis to interpret and summarize product reviews.

API Integration: Develop APIs for integrating with the Temu platform, ensuring smooth data retrieval and referral code usage.

Cross-Platform Compatibility: The assistant should be compatible with both iOS and Android platforms.

Scalability and Performance: Design the system for high scalability and performance, considering a potentially large user base.

Testing and Quality Assurance: Rigorous testing must be conducted, including unit testing, integration testing, and user acceptance testing (UAT).

Documentation and Training: Provide comprehensive documentation and training materials for end-users and administrators.