Sahayak

• 

  The Saviour

Inspiration

In India and across the globe, the unavailability of blood at the right time has led to countless avoidable tragedies. Despite the growing number of voluntary donors, there exists a disconnect between patients in need, hospitals, and potential donors. We were inspired to build Sahayak to bridge this critical gap using technology. Our vision is to save lives with intelligent, real-time blood donor matching — ensuring that no one loses a loved one due to delays or lack of information.

What it does

Sahayak is an AI-driven platform that connects patients and hospitals with suitable blood donors in real-time. Here’s what it does:

🩸 Matches patients with donors based on blood type, location, and urgency.

🏥 Integrates with hospitals to streamline donor requests.

📍 Uses geolocation to find nearby, available donors.

🔔 Sends alerts/notifications to verified donors when someone nearby needs their help.

🔐 Maintains donor privacy and consent, ensuring ethical data usage.

📊 Uses AI to predict demand patterns for different blood types in various regions (future enhancement).

How we built it

We used a modular, full-stack architecture with the following components:

Frontend: HTML, CSS, JavaScript for clean, responsive user interaction.

Backend: Node.js + Express for handling APIs, user requests, and data logic.

Database: MongoDB to store user details, hospital records, and donor information.

AI/ML Layer: A basic model using Python + Pandas + Scikit-learn to prioritize donor matching (blood group, location, availability).

Email/OTP System: NodeMailer integrated for donor verification and emergency alerts.

Deployment: Hosted on Render (backend) and Netlify (frontend) for quick access

Challenges we ran into

Every project has its hurdles, and Sahayak was no different:

📡 Real-time matching logic: Developing an efficient algorithm to handle location + blood group + availability took time and iterations.

🔐 Data privacy: Ensuring personal information of donors is kept secure while still enabling effective communication.

🧪 Testing emergency flows: Simulating real-life urgency scenarios in a demo environment was challenging.

🌍 Handling large scale: Designing for scalability when the number of users and hospitals increases in the future.

Accomplishments that we're proud of

✅ Successfully built a working end-to-end prototype that connects donors with hospitals.

📬 Integrated a functional OTP verification system to ensure donor authenticity.

🤖 Developed a basic AI model for donor matching based on multiple parameters.

🧩 Created an intuitive, user-friendly interface suitable for both hospital staff and general users.

🚀 Deployed live on the internet with full-stack functionality in a short time frame.

What we learned

🤝 Team collaboration matters: Coordinating frontend, backend, and AI workflows was a great lesson in teamwork.

🔍 Importance of UX in health-tech platforms. We had to think from the user's perspective — simplicity and clarity are key.

📡 Working with real-time data (like geolocation and emergency alerts) taught us how to handle asynchronous flows and errors.

🧠 AI integration isn’t always complex: Even a simple predictive model can drastically improve the utility of a project.

What's next for Sahayak

Sahayak is just getting started. Here's what’s ahead:

📱 Launch a mobile app to reach rural areas and provide better notifications.

🔬 Use advanced AI to forecast blood shortages in regions based on season, events, and history.

🏥 Integrate with hospital systems like HMIS and blood banks to automate requests.

🧾 Add donor history & reward systems to encourage repeated donations.

🌍 Collaborate with NGOs and healthcare partners for real-world deployment and testing.

📈 Expand database scalability and move toward using cloud solutions like AWS or Azure for larger-scale operations.

Built With

• amazon-web-services
• mern
• restapi