SustainLabs

Inspiration Our inspiration stemmed from seeing how rural economies are constrained by unreliable energy systems despite the growing availability of renewable solutions. We wanted to build a platform that not only accelerates clean energy adoption but also empowers rural communities through trusted, efficient, and resilient infrastructure.

What it does SustainLabs unifies renewable energy management into a single platform that: ✅ Provides real-time energy flow and consumption visualization ✅ Offers AI-driven recommendations for optimization and predictive maintenance ✅ Enables decentralized energy trading with blockchain-verified transactions ✅ Monitors disaster events and equipment health ✅ Facilitates smartphone-based health monitoring without wearables ✅ Supports interactive learning for renewable energy skills

How we built it We used a full-stack approach:

Frontend: React, TypeScript, Framer Motion, Recharts for the interactive dashboard

Backend: Python (AI models), Node.js (APIs, real-time comms via Socket.IO)

Databases: Supabase, MongoDB, TimescaleDB for scalable data management

AI: TensorFlow, Google Generative AI, Groq LPU for real-time insights

Blockchain: Multi-chain abstraction layer supporting smart contracts, transactions, and verifications

Hosting: AWS, Vercel

Challenges we ran into 🔹 Integrating 9 blockchains — we overcame this by designing a custom blockchain abstraction layer. 🔹 Real-time processing for thousands of IoT devices — we leveraged edge computing and Merkle trees for integrity and speed. 🔹 Ensuring fast AI analysis — we integrated Groq LPU and federated learning for distributed model updates. 🔹 Simplifying the user experience — we adopted progressive disclosure in dashboards based on user roles.

Accomplishments that we're proud of 🏆 Successfully created a unified platform integrating AI, blockchain, and edge computing 🏆 Reduced predictive maintenance downtime by up to 73% 🏆 Developed a role-based, context-aware dashboard that adapts to user needs 🏆 Established partnerships with tech providers (e.g., Groq, AWS) for future scaling

What we learned 💡 How to efficiently combine multi-chain blockchain systems into one application 💡 How to design AI models that balance accuracy with real-time performance 💡 The importance of human-centered design when dealing with complex data 💡 How to coordinate a multidisciplinary team across AI, blockchain, and IoT domains

What's next for SustainLabs 🚀 Enhance AI models for even better predictive capabilities 🔒 Introduce zero-knowledge proofs for private energy data sharing 🌍 Expand localization for other rural markets globally 🔋 Integrate smart battery management with degradation prediction 🤖 Move towards autonomous, self-healing grid management