Mumei
Privacy-First Feedback, Powered by AI.
About
The problem it solves Mumei addresses critical challenges in feedback collection within Web3 by ensuring **user privacy**, offering **fair crypto rewards**, and providing **actionable insights** for companies. Traditional feedback systems often compromise user anonymity or fail to fairly compensate contributors. Mumei solves this by anonymizing feedback, ensuring it cannot be traced back to the user. This is achieved through **blockchain technology** and **AI-powered evaluations** that maintain privacy while ensuring users can still be rewarded for their valuable insights. For users, Mumei offers a simple and secure platform to submit feedback without fear of identity exposure. After a user interacts with a service, they submit feedback, which is then anonymized and processed through an AI agent. The AI evaluates the feedback’s **usefulness**, calculates a reward, and distributes it via **CDP SDK**, ensuring crypto-based compensation. This process remains fully automated, eliminating manual intervention and ensuring transparency and fairness. For companies, Mumei filters out irrelevant or biased feedback using AI, providing only actionable insights, which saves time and resources. The system also guarantees the security of data through **blockchain technology**, ensuring feedback cannot be altered and rewards are distributed as intended. The rewards are aggregated and anonymized, further preserving user privacy. Mumei thus fosters a **privacy-first** feedback loop, enhances user participation through incentivization, and enables companies to gather high-quality, objective feedback without compromising privacy. This makes feedback collection in Web3 more efficient, fair, and trustworthy for both users and companies.
How it's made
Challenges we ran into The main challenges we faced while building Mumei were related to conceptualizing and implementing core features, particularly around user privacy, feedback anonymization, and reward distribution. **Ensuring Privacy and Anonymization**: Ensuring that user feedback remained anonymous was a significant hurdle. We needed to design a system that could collect feedback while keeping user identities completely private. Using blockchain technology, we were able to store feedback in a secure, immutable way that prevented any links to the user. **Designing the AI Agent**: Creating an AI agent to assess feedback quality while keeping it anonymous was another challenge. We needed the agent to evaluate feedback without revealing personal information. After much testing and refinement, we developed an agent that could debias and evaluate feedback effectively. **Reward Distribution Mechanism**: The reward system needed to fairly compensate users based on the quality of their feedback without exposing their identity. We built an automated system that aggregated rewards and distributed them while preserving privacy, ensuring users received fair compensation based on feedback usefulness.