AI based Smart Allocation Engine for PM Internship Scheme

Inspiration: The idea stemmed from observing the inefficiencies and biases in manual internship allocations across large-scale government programs. Inspired by the vision of equitable access and optimal resource utilization, I wanted to build a system that could intelligently match interns to opportunities based on merit, preferences, and institutional needs—without human bottlenecks.

What I Learned: This project deepened my understanding of AI-driven decision systems, especially in the context of real-world constraints. I explored:

Algorithmic fairness and ethical allocation
Data normalization and scoring techniques
Backend logic for scalable matching engines
UI/UX principles for intuitive dashboards

How I Built It:

Designed a matching algorithm using weighted parameters like skill sets, location preferences, and institutional requirements.
Implemented the logic in C++, integrating with a simple frontend for admin and applicant views.
Used mock datasets to simulate real-world scenarios and test allocation accuracy.
Incorporated feedback loops to refine match quality over time.

Challenges Faced:

Balancing fairness with optimization—ensuring high-performing candidates were placed without sidelining others.
Handling incomplete or ambiguous data entries during simulation.
Designing a system that could scale while remaining transparent and explainable to stakeholders.