Submission by Ismail

PrepMate — AI Mock Interviewer PrepMate is a voice-driven mock interview platform that helps job seekers practice technical and behavioral interviews with a realistic AI interviewer. You paste a job description, and PrepMate generates 5 tailored interview questions, conducts a full voice conversation with follow-up questions based on your answers, then delivers a scored debrief with per-question feedback, strengths, and areas to improve. The problem it solves Most interview prep is passive — reading questions, watching videos, rehearsing in your head. PrepMate makes it active. You speak your answers out loud to an AI that listens, pushes back, and evaluates you — the same way a real interviewer would. The gap between knowing an answer and being able to articulate it under pressure is where most candidates fail. PrepMate closes that gap. How it uses ElevenLabs ElevenLabs powers the entire voice layer of the interview. The AI interviewer speaks every question and acknowledgment using ElevenLabs TTS, making the experience feel like a real call rather than a text interface. When the candidate responds, ElevenLabs STT transcribes the answer in real time, feeding it into the follow-up and evaluation pipeline. The choice to use the TTS/STT APIs directly — rather than the Conversational Agent — was intentional: PrepMate requires deterministic session state to map each answer to its question for per-question scoring. The raw APIs gave full control over the interview flow while keeping ElevenLabs' voice quality at every step. How it uses Kiro PrepMate was built entirely using Kiro's spec-driven development workflow. Before writing a single line of code, the full feature was specced out in Kiro — requirements, architecture, API contracts, and component design. Kiro's AI then worked through the implementation task by task, from the FastAPI backend to the Next.js frontend to the design system. Kiro's steering files were used to encode project conventions, tech stack decisions, and design principles directly into the workspace, so every code change stayed consistent with the product vision. The result is a production-quality app built in a fraction of the time it would take manually.