Inspiration
Alumni networks contain a huge amount of valuable knowledge, career experience, and opportunities, but this information is often scattered across spreadsheets, social media groups, messaging platforms, and disconnected college systems.
We wanted to build a single platform that could bring students and alumni together in a meaningful way—not just as a directory, but as an active career and engagement ecosystem.
This led us to AlumniConnect AI, a centralized platform designed to connect students with alumni mentors, career opportunities, events, fundraising initiatives, and personalized career insights.
What it does
AlumniConnect AI provides a centralized digital platform for managing and engaging an institution's alumni community.
For Students
- Discover and connect with alumni mentors based on skills, department, and career interests.
- Receive AI-assisted mentor recommendations.
- Send and manage mentorship requests.
- Communicate with mentors and participate in video meetings.
- Discover relevant alumni and student events.
- Register for upcoming events.
- Track mentorship and event activity from the dashboard.
- Receive personalized career recommendations.
For Alumni
- Maintain and update their professional profile.
- Indicate whether they are available for mentorship.
- Discover and manage mentorship requests.
- Communicate with students through messaging and video meetings.
- Create alumni-focused events.
- Participate in fundraising campaigns.
- Track their engagement and activities.
For Administrators
- Manage students and alumni.
- Create and manage institutional events.
- Manage mentorship activities.
- Manage fundraising campaigns and donations.
- Monitor reports and analytics.
- View career and placement insights.
- Manage platform settings and roles.
The platform also provides AI-powered career analytics and prediction capabilities to help transform alumni data into useful career insights.
How we built it
We built AlumniConnect AI as a full-stack microservices-based application.
Frontend
- React 19
- Vite
- React Router
- Ant Design
- Axios
- CSS Modules
- React Icons
Backend
- Spring Boot microservices
- Spring Cloud API Gateway
- JWT-based authentication
- TiDB Cloud / MySQL-compatible database
The backend is divided into independent services for authentication, mentorship, events, fundraising, analytics, and AI-powered functionality.
AI Layer
A separate FastAPI-based Python service handles AI-related career analytics, recommendations, and prediction functionality.
Additional Integrations
- Razorpay for fundraising payments
- 8x8 JaaS for video meetings
- Brevo SMTP for email communication
The application was containerized using Docker and deployed using Vercel for the frontend and Render for the backend microservices, with TiDB Cloud as the managed database.
Challenges we ran into
One of our biggest challenges was moving from a local development environment to a distributed cloud deployment.
Running multiple Spring Boot services, an API Gateway, and a separate AI service introduced challenges involving service-to-service communication, environment variables, CORS, authentication, database connectivity, and deployment configuration.
We also had to handle cold starts on Render's free-tier services. Services could sleep when inactive, causing temporary 502/503/504 errors. We improved the frontend with independent requests, bounded retries, loading states, and non-destructive state handling so that temporary service wake-up delays would not appear as permanent application failures.
Another challenge was migrating our existing Oracle database data to TiDB Cloud while preserving existing records and BCrypt password hashes. We validated the migrated data and ensured that the application continued to work with the new database.
We also had to carefully separate the concepts of an event's organizer and its creator. We introduced an explicit createdByType field so events created by administrators and alumni could be identified reliably instead of inferring ownership from organizer names.
Accomplishments that we're proud of
We are proud that AlumniConnect AI evolved from a concept into a working, deployed platform with multiple interconnected modules.
Some of our key accomplishments include:
- Built a complete alumni engagement ecosystem rather than just an alumni directory.
- Implemented AI-assisted mentor recommendations.
- Added mentorship request, messaging, meeting, and history workflows.
- Added mentor availability controls for alumni.
- Built student, alumni, and administrator dashboards.
- Implemented event creation, eligibility, registration, and tracking.
- Integrated Razorpay for fundraising.
- Integrated JaaS for video-based mentorship meetings.
- Added career analytics and prediction capabilities.
- Migrated existing Oracle data to TiDB Cloud while preserving user credentials and records.
- Deployed the frontend and backend microservices to the cloud.
- Designed the system to handle transient failures and microservice cold starts more gracefully.
Most importantly, we learned how to take a multi-module application from local development through database migration, containerization, deployment, debugging, and production testing.
What we learned
This project taught us that building a real-world application is much more than writing frontend and backend code.
We learned how to design and work with microservices, manage API Gateway routing, handle authentication across distributed services, migrate databases, configure cloud environments, and debug production-only problems.
We also learned the importance of designing for failure. A service being temporarily unavailable should not necessarily make the entire application appear broken. Handling retries, timeouts, loading states, and partial failures became an important part of our development process.
On the AI side, we learned how AI can complement traditional application logic instead of replacing it. AI-powered recommendations and career analytics become much more useful when they are integrated into a well-structured platform with reliable data and clear user workflows.
What's next for AlumniConnectAI
We want to continue evolving AlumniConnect AI into a more intelligent and scalable alumni ecosystem.
Our future plans include:
- Improving AI-based mentor matching with richer career and skill profiles.
- Adding more personalized career-path recommendations.
- Expanding alumni networking and professional opportunity discovery.
- Introducing richer career outcome analytics.
- Improving recommendation accuracy using real engagement data.
- Adding more institution-level analytics for administrators.
- Supporting larger alumni communities with scalable search and pagination.
- Expanding integrations with professional and career platforms.
- Adding more automation for alumni engagement and communication.
Our long-term goal is to make AlumniConnect AI a platform where students can discover guidance, alumni can give back, and institutions can turn their alumni network into a continuously growing ecosystem of knowledge and opportunities.
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