CrackIt: An AI-Powered Interview Preparation and Performance Analysis Platform

Abstract: In today’s competitive job market, effective interview preparation plays a critical role in improving employment opportunities for candidates. Traditional preparation methods such as static question banks or manual mock interviews often fail to provide personalized feedback and real-time performance evaluation. This research presents CrackIt, an AI-powered interview preparation platform designed to simulate realistic interview environments using advanced web technologies and Large Language Models (LLMs). The system integrates resume analysis, AI-driven interview simulations, performance analytics, and automated report generation into a unified platform. CrackIt leverages modern frameworks including React, FastAPI, Supabase, and Groq AI models to deliver adaptive interview experiences tailored to individual users. The platform also incorporates secure authentication, interactive dashboards, and downloadable performance reports to enhance user engagement and tracking. The proposed solution aims to bridge the gap between theoretical preparation and real-world interview scenarios by providing intelligent feedback and personalized recommendations. Experimental implementation demonstrates that the platform effectively improves interview readiness through continuous evaluation and structured insights.

Keywords: Artificial Intelligence, Interview Simulation, Resume Analysis, Large Language Models, Web Applications, Performance Analytics