💧 Hydroniq: Making Water Problem Reporting Simpler with Open-Source AI
We built Hydroniq, a water intelligence platform designed to make community water problem reporting and tracking easier. Our goal is to combine open-source AI with a practical social and environmental challenge: reducing water wastage and making water-related problems easier to organize and monitor.
What I Built
Hydroniq helps organize reports about leaking public taps, broken handpumps, and other water infrastructure problems in one place.
Reporting these problems is only the first step. Organizing complaints, summarizing their details, identifying missing information, and tracking their progress can also involve repetitive administrative work.
Hydroniq aims to simplify that workflow.
Key Features
- Community Reporting: Submit water-related problems with descriptions and location details.
- Report Tracking: Track reports through Pending, In Progress, and Resolved statuses.
- Live Dashboard: View total submissions, pending reports, and resolved issues.
- Impact Analytics: Explore which types of water problems are reported most frequently.
- Area Monitoring: Organize reports by state, district, and location.
- AI-Assisted Analysis: Use Gemma to suggest issue categories, summarize reports, identify missing information, and recommend next steps for human review.
The idea behind Hydroniq is simple: reduce repetitive work so people can spend more time understanding problems and deciding what needs attention.
AI-generated analysis is only a suggestion. It does not independently verify a complaint or replace human decision-making.
Demo
🌐 Live Demo: https://hydroniq-theta.vercel.app
The deployed application connects a React frontend to a FastAPI backend and a cloud-hosted MySQL database. Reports can be submitted, stored, retrieved, and displayed on the dashboard.
Current limitation: The AI-assisted analysis works with a local Ollama and Gemma setup during development. Hosted AI inference is still a future improvement.
Code
💻 GitHub Repository: https://github.com/mdnawedullah09/Hydroniq
Hydroniq is open source, and we welcome feedback, ideas, and contributions from the community.
How I Built It
We used the following technology stack:
- Frontend: React and Vite
- Backend: Python and FastAPI
- Database: MySQL hosted on Aiven
- Open-Source AI: Gemma 3 4B through Ollama
- Frontend Deployment: Vercel
- Backend Deployment: Render
- Version Control: Git and GitHub
How the AI Workflow Works
- A user provides a water problem report, including its issue type, location, and description.
- The report information is sent to the Gemma model through Ollama in the local development setup.
- The model suggests a category, generates a short summary, highlights missing information, and proposes a practical next step.
- A human reviewer can use these suggestions to help decide what to examine next.
The model is not treated as an authority. Human review remains important before any official action is taken.
Building Hydroniq also helped us learn how a frontend, backend APIs, a persistent cloud database, and a local AI model can fit together in a full-stack application.
Why Does Open Innovation Matter?
Open innovation matters because developers should have the freedom to experiment with AI, understand its limitations, and adapt it to real-world needs.
Using the open-weight Gemma model through Ollama allowed us to explore AI-assisted report analysis locally instead of making a closed AI API the core of our workflow.
This gave us more control over experimentation and prompt design, while helping us understand the practical considerations of running a model locally.
For a project focused on community welfare and environmental sustainability, accessible and adaptable AI tools can help developers build solutions around local problems.
Open-source technology also makes it easier for others to inspect the code, learn from the implementation, and contribute improvements.
My Agent Session
We do not have a DevRelay agent session to share yet.
The Lazy Payoff
Hydroniq is designed to reduce the repetitive steps involved in organizing water problem reports.
Instead of manually preparing an initial summary for each complaint, AI-assisted analysis can suggest a category, summarize the description, flag missing details, and recommend a next step for a human reviewer.
The reporting, tracking, dashboard, analytics, and database workflow has been implemented. However, we have not yet measured time savings with real community users, so we will not claim a specific number of minutes saved.
Our next goal is to test the workflow with real users and measure whether it reduces the effort required to review and organize reports.
For us, “Slow and Steady” means building a useful foundation first, testing it honestly, and improving it step by step rather than making unsupported claims about impact.
Prize Categories
Our project focuses on:
- Open-source AI
- Practical AI-assisted workflow automation
- Social impact and environmental sustainability
We will choose the official partner prize categories that match the challenge rules before submitting.
Team
We worked together on Hydroniq, combining our ideas and development efforts to build a water intelligence platform that uses open-source AI to support community water problem reporting and water conservation.
- MD Nawedullah Saif — Co-Developer
- Jaya Patel — Co-Developer
We collaborated on the project and shared responsibility for bringing Hydroniq from an idea to a working application, combining full-stack development with our goal of supporting communities and protecting water resources.
Final Thoughts
Hydroniq started with a simple idea: make water-related problems easier to report, organize, and monitor.
This project has been a learning experience in full-stack development, deployment, database integration, and experimenting with open-source AI in a practical workflow.
We hope to keep improving Hydroniq, make its AI-assisted features more accessible, and explore how technology can help communities protect an essential resource: water.
We would love to hear your feedback and suggestions. What features would make a community water reporting platform more useful?
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