Inspiration
Water scarcity is a growing global challenge, especially in urban areas where commercial buildings consume massive amounts of water. Inspired by the potential of rainwater harvesting to reduce costs and environmental impact, we built RainUSE Nexus to democratize access to sustainable water solutions. By leveraging AI and data analytics, we aim to help businesses identify high-value opportunities for rainwater systems, promoting both financial savings and ecological responsibility.
What it does
RainUSE Nexus is an automated prospecting dashboard that identifies and scores commercial buildings across the US for rainwater harvesting potential. It analyzes roof size, rainfall data, cooling tower presence, and financial metrics to generate a viability score. Users can explore an interactive map, apply filters (by state, roof size, viability, etc.), view top opportunities, and access detailed building reports. The platform integrates a Go-based logistics service for deployment planning, providing a complete pipeline from prospecting to implementation.
How we built it
We developed a full-stack application with a Python FastAPI backend for data processing and API endpoints, an HTML/CSS/JavaScript frontend for the interactive dashboard, and a Go microservice for logistics clustering. The backend loads mock building data, enriches it with environmental and financial calculations, and serves it via REST APIs. The frontend uses Leaflet for mapping and fetches data asynchronously. We ensured cross-platform compatibility by handling virtual environments and file paths for Windows/Git Bash. The project uses open-source libraries like FastAPI, Uvicorn, and Leaflet, with data stored in JSON format.
Challenges we ran into
Setting up the development environment was tricky due to cross-platform differences—virtual environment activation failed in Git Bash on Windows, requiring adjustments to use absolute paths. File structure mismatches between the README and actual code caused import errors, and we had to reorganize directories and fix static file paths. Integrating the Go service with the Python backend involved handling CORS and ensuring services started correctly. Additionally, simulating realistic building data and scoring algorithms demanded careful validation to avoid errors.
Accomplishments that we're proud of
We successfully built a fully functional, multi-service dashboard that processes 230+ building records in real-time, delivering an intuitive user experience with interactive maps, dynamic filters, and detailed analytics. The AI-driven viability scoring engine combines water potential, ROI, ESG factors, and CV confidence into a comprehensive metric. Achieving cross-platform compatibility (macOS/Linux and Windows) and integrating a Go microservice for logistics planning demonstrates robust engineering. The project is hackathon-ready, with clean code, documentation, and a professional UI.
What we learned
This project deepened our understanding of full-stack development, from API design and asynchronous data fetching to cross-platform deployment challenges. We gained experience in data enrichment pipelines, integrating AI scoring with real-world metrics, and building microservices with Go. Debugging environment-specific issues taught us the importance of thorough testing across platforms. We also learned about sustainable tech applications, realizing how data-driven tools can drive environmental impact.
What's next for Access Rain Nexus
We're excited to integrate real satellite imagery and CV models for accurate cooling tower detection, replacing mock data with live APIs. Expanding to global datasets, adding user authentication for saved prospects, and deploying to the cloud (e.g., AWS or Vercel) are key goals. We'd love to partner with water utilities or environmental NGOs to pilot real-world installations, and explore mobile app development for on-the-go prospecting. Future enhancements include predictive analytics for climate change impacts and automated proposal generation.
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