Challenge: Ensuring User Privacy in a Multichain Architecture

Hurdle: A major challenge was maintaining user privacy for sensitive data while leveraging the transparency of public blockchains for reporting and voting. How do you verify users and handle their data without exposing it on a public ledger?
Solution: The team implemented a hybrid architecture. They used the Oasis Sapphire Network as a dedicated privacy layer to manage user verification and handle sensitive data with encrypted storage. This allowed them to keep user data private and encrypted while using various public L2 chains for transparent, non-sensitive interactions like reports and votes.

Challenge: Spam Prevention and Content Moderation

Hurdle: In any public reporting platform, preventing spam and ensuring the credibility of reports is a significant challenge. A system open to anyone could be abused with false or low-quality submissions.
Solution: Rather than relying on centralized moderation, the team built a decentralized, incentive-based system. They implemented a sophisticated point system that applies penalties for false reports and offers token rewards for high-quality, verified contributions. This community-driven validation model reduces bias and creates a self-moderating ecosystem.

Challenge: Integrating a Specialized AI Model

Hurdle: The project required an AI that could do more than just recognize objects; it needed to understand context, assess severity, and ensure report descriptions matched the image content.
Solution: The team utilized a fine-tuned Hyperbolic Pixtral-12B Vision Model. This specialized model was trained to analyze both images and text to categorize issues, suggest solutions, and assign priority levels, ensuring that the AI-powered analysis was accurate and relevant to the platform's goals.