HireLite

Inspiration

Applying to jobs today is frustrating, time-consuming, and often discouraging. Candidates spend hours tailoring resumes, searching through endless listings, and still feel like their applications disappear into a void. We wanted to rethink this experience from the ground up.

Inspired by the simplicity and engagement of swipe-based apps, we asked: What if finding a job felt as intuitive as swiping—and what if AI could handle the hardest part of applying? That idea became HireLite.

What it does

HireLite is a streamlined, swipe-based job application platform that helps users discover opportunities quickly and apply smarter.

📱 Swipe through jobs in a clean, intuitive interface ⚡ Apply instantly with a single action 🤖 Use AI to tailor your resume to each job 📊 Get insights like match scores and missing keywords

Instead of spending hours rewriting resumes, users can focus on finding the right opportunities—while AI optimizes their chances behind the scenes.

How we built it

We built HireLite using a modern, fast-moving stack designed for rapid iteration:

Frontend: Expo (React Native) for a mobile-first, swipe-based UI Backend: Node.js + Express API for handling users, jobs, and applications Database: MongoDB for flexible, scalable data storage Hosting: Railway (backend) + Vercel (web deployment) AI Integration: OpenAI for resume optimization and job matching

We started with a rapid prototype generated using Appifex and Replit, then transitioned from mock data to a fully connected backend with real user, job, and application flows. The AI feature was layered on top to enhance resumes dynamically based on job descriptions.

Challenges we ran into Transitioning from mock data to real data: Moving from a fully mocked frontend to a real database introduced issues with API connections, state management, and persistence. Deployment for the first time: Getting the app live required learning how to host a backend, connect environment variables, and ensure frontend-backend communication worked outside of localhost. API and mobile networking issues: Handling CORS, replacing localhost URLs, and ensuring requests worked on real devices took careful debugging. Balancing features vs. time: We had to prioritize core functionality (authentication, job swiping, applications, AI) over additional features to ensure a complete and working demo. Accomplishments that we're proud of 🚀 Successfully built a full-stack application with real-time data flow 🔗 Connected a mobile frontend to a live backend and database 🤖 Implemented AI-powered resume tailoring with meaningful output ⚡ Created a smooth, intuitive swipe-based job discovery experience 🌐 Deployed a live, accessible version of the app What we learned How to transition from prototype to production-ready architecture How to deploy full-stack applications and manage environment configuration How to integrate AI into a real product in a meaningful, user-facing way The importance of breaking problems into smaller, solvable pieces How to collaborate effectively under time pressure What's next for HireLite

We see HireLite as more than just a hackathon project—it has real potential to improve how people find jobs.

Next steps include:

📈 Improving AI accuracy with deeper resume analysis and personalization 💬 Adding real-time messaging between applicants and employers 📊 Expanding match scoring with more advanced job-fit metrics 🔐 Enhancing authentication and security (e.g., password hashing, sessions) 🌍 Scaling the platform to support more users and job sources

Our goal is to turn HireLite into a powerful, AI-driven platform that makes job searching faster, smarter, and more human.

Built With

• appifex
• claudeai
• expo.io
• express.js
• github
• javascript
• mongodb
• node.js
• openai
• railway(backend)
• react-native
• replit
• typescript