Preplify: AI Interview Prep
A production-grade, cloud-native AI interview preparation platform built on Next.js and TypeScript, designed to serve thousands of concurrent users with real-time, personalized interview experiences.
Tech Stack
Status
Production Ready
Type
Enterprise platform
Architected and delivered a scalable headless Next.js + TypeScript platform supporting thousands of concurrent users.
Integrated GraphQL and REST-based AI services with robust error handling and API versioning patterns.
Designed 30+ reusable UI components with Tailwind CSS, establishing a consistent design system.
Implemented state management using Zustand for seamless tracking of interview progress.
Integrated LLM APIs (OpenAI / Gemini) for real-time, dynamic interview question generation.
Built real-time communication functionality with WebSockets for interactive mock interviews.
Optimized performance through SSR, ISR, and caching strategies, improving page load times by 35%.
Established comprehensive CI/CD pipeline with GitHub Actions and Docker-based deployments.
Preplify: AI Interview Prep
Overview
Preplify is an AI-assisted interview preparation platform designed to make practice more realistic, personalized, and actionable. Instead of relying on static question banks, the platform generates contextual prompts, supports interactive sessions, and gives users a structured environment to improve over time.
Product Goals
The main objective was to build a preparation experience that felt closer to a live interview while remaining fast, reliable, and easy to extend. That required combining multiple AI and application concerns into one cohesive product:
- ›dynamic question generation
- ›personalized feedback loops
- ›real-time practice interactions
- ›progress tracking and session continuity
- ›reliable auth, deployment, and observability foundations
What I Built
- ›Architected the application using
Next.jsandTypeScriptto support fast product iteration and scalable delivery. - ›Integrated both
GraphQLandRESTservice layers for AI workflows, user data, and supporting product operations. - ›Connected LLM providers for dynamic interview question generation and personalized response feedback.
- ›Built WebSocket-powered interactive interview experiences for low-latency practice flows.
- ›Designed a reusable UI system with
Tailwind CSS, enabling consistent product surfaces across onboarding, dashboards, and interview modules. - ›Used
Zustandto manage multi-step interview state, active session context, and progress continuity.
Technical Highlights
AI Workflow Integration
The platform orchestrates AI-backed question generation and feedback experiences around user context, role specialization, and ongoing practice history. Reliability was important, so the system design emphasized predictable request patterns, graceful handling of AI responses, and clear UI feedback during loading and retries.
Performance Strategy
Because AI products often accumulate heavy client logic, the frontend emphasized SSR, ISR, caching, and code-splitting to preserve responsiveness. These optimizations improved page load times by 35% while keeping complex feature areas maintainable.
Delivery Foundations
To support product stability, the project included automated testing and CI/CD workflows with Jest, Playwright, GitHub Actions, and Docker-based deployment practices.
Outcomes
- ›35% faster page loads through rendering and caching optimizations
- ›40% faster frontend feature development thanks to reusable component patterns
- ›scalable, interactive mock interview experiences backed by real-time communication
- ›stronger product extensibility for future AI-assisted preparation modules
Notes
Current screenshot paths are placeholder paths that will be replaced with final approved Preplify visuals later.