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 is built in clear layers so each concern can scale independently:
NexPrep turns passive interview prep into active, pressure-tested practice — candidates build technical confidence and communication fluency at the same time. The project demonstrates end-to-end product thinking: identifying a real user pain point, designing the architecture, shipping the implementation, and iterating from feedback — with depth in LLM integration, real-time systems, and user-centered design.