RainUSE Nexus Grundfos · Automated Water Prospecting Engine

Inspiration

What inspired me was the opportunity to take a real industry problem and turn it into a practical technical solution. I was especially interested in combining sustainability, geospatial data, and AI into a tool that could help identify real-world water reuse opportunities more efficiently.

What it does

RainUSE Nexus is an automated water prospecting engine. It scans candidate buildings, uses satellite imagery and computer vision to evaluate rooftop suitability and cooling tower signals, and combines that with water, environmental, and business context to rank promising sites for rainwater harvesting and reuse.

How we built it

We built it as a full-stack prototype with a web frontend and a Python backend. The frontend handles the user workflow, maps, and results dashboard, while the backend handles building discovery, satellite image retrieval, computer vision, scoring, and annotated output generation.

Challenges we ran into

One major challenge was making computer vision work reliably on real satellite images, especially for small rooftop features. Another challenge was integrating the frontend and backend cleanly so the correct CV results, images, and site data appeared consistently across the workflow.

Accomplishments that we're proud of

We are proud that we built an end-to-end working prototype rather than just a concept. We are also proud that the system is explainable: it does not only rank sites, but also shows visual evidence and supporting metrics behind each result.

What we learned

We learned that solving this problem is not just about AI. It also requires strong integration across data, backend logic, frontend design, and user experience. We also learned how important debugging, consistency, and explainability are in building a practical decision-support tool.

What's next for RainUSE Nexus Grundfos · Automated Water Prospecting Engine

The next step is to make the platform more robust and production-ready by improving CV accuracy, strengthening data enrichment, and refining the full scan-to-report workflow. We would also like to expand it into a more complete lead-intelligence tool for real-world prospecting and decision support.

Built With

• python
• render