I'm starting a full-stack project aimed at a problem I kept thinking about: when a recruiter interviews hundreds of students in a day, how fair can the ratings really stay?
The problem
Think about placement season. One recruiter might interview 50-100+ students back to back. By interview #60, it's genuinely hard to judge everyone the same way. A tired interviewer might mark a correct answer lower than they would've an hour earlier not on purpose, just human fatigue. This isn't about a bad recruiter, it's just a hard problem when you're rating people at that volume.
Re-listening to every recording to double-check is the obvious fix but also genuinely frustrating and time-consuming when there are that many interviews.
What I'm building
Frontend — the recruiter/HR logs in and uploads the interview recording (audio or video, online or offline).
Backend — built with Java and Spring Boot:
- Whisper API converts the recording into a transcript
- Gemini API goes through that transcript and evaluates the actual answers — how accurate, how complete, how well the candidate understood the topic
Database — stores the transcript and the generated rating/documentation per candidate, so the recruiter has something consistent to refer back to instead of relying purely on memory or handwritten notes from hours earlier.
What this is not
This is not the AI making a hiring decision. That stays 100% with the recruiter.
What the tool does is give the recruiter a consistent, evidence-based reference how accurate each answer actually was so the final call isn't just based on how tired the interviewer was by that point in the day. The recruiter still decides if someone's the right fit. The AI just helps make sure the rating going into that decision is based on what the candidate actually said, not on interview fatigue.
Where I'm at right now
Early stage designing the database schema and setting up the Spring Boot backend before wiring up the Whisper and Gemini integrations.
Will share progress as I build this out.
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