The mentAImage LevelUP project aims to develop a mini MVP that assesses burnout risk using multimodal inputs, including short text reflections, 10-second voice notes, and simple drawings. The goal is to create a lightweight inference pipeline that generates a "burnout risk snapshot" and displays role-based dashboards for Users, HR, and Coaches. This project is divided into three tracks, each managed by an intern specializing in ML & Data Science, Backend & Data Engineering, and Frontend & UX Engineering. The project emphasizes the use of anonymized or synthetic data and provides limited but structured mentorship. The initiative allows learners to apply their classroom knowledge in React, FastAPI, and machine learning to build a functional prototype that can be completed in under 80 hours.

The project deliverables include a demo-ready prototype with responsive React dashboards, FastAPI endpoints, and a modular data pipeline. Additionally, baseline ML models for text sentiment, voice tone, and drawing features will be produced and fused into a simple burnout risk score. The final output will also consist of comprehensive documentation, a short presentation, and a recorded walkthrough demonstrating the prototype's functionality.