Mock interview platform simulating human voices with real-time feedback & ATS checker.
NexPrep AI is a full-stack AI-powered mock interview platform designed to help job seekers prepare for technical and behavioral interviews. The platform simulates realistic interview scenarios using voice synthesis technology, enabling candidates to practice with human-like AI interviewers that adapt to their responses in real time.
Unlike passive preparation tools, NexPrep AI creates an active, pressure-tested environment where candidates receive immediate, structured feedback on their answers — covering content quality, communication clarity, and keyword alignment with job descriptions.
Traditional interview preparation is passive — candidates read questions from books or watch videos, but never experience the pressure of a real conversation. This gap between preparation and reality leads to interview anxiety and poor performance even for technically strong candidates.
NexPrep AI bridges this gap by creating a live, simulated interview environment that mirrors the pacing, follow-up questions, and evaluative pressure of actual interviews. By practicing in a realistic setting, candidates build both technical confidence and communication fluency simultaneously.
Built with Next.js App Router for server-side rendering and optimal performance. Firebase handles authentication and real-time data synchronization across sessions. MongoDB stores interview sessions, question banks, and user analytics with efficient query patterns. The AI layer uses Google's Gemini API for generating contextual follow-up questions and evaluating answer completeness. Web Speech API enables voice input and output directly in the browser without additional plugins.
NexPrep AI demonstrates the practical integration of LLM APIs with real-time web technologies to solve a genuine pain point in the job market. The platform showcases end-to-end product thinking — from identifying a user problem, designing the solution architecture, building the technical implementation, and iterating based on real user feedback. The project highlights skills in AI integration, real-time systems design, and user-centered product development.