Swiped-In: Reinventing Hiring with AI and Swipe-Based Matching

Inspiration

The traditional hiring process is outdated—filled with endless applications, impersonal ATS filters, and slow responses. Inspired by the simplicity and engagement of swipe-based dating apps, we set out to transform hiring into a faster, more interactive experience. Our goal was to create a seamless job-matching platform where candidates and recruiters connect effortlessly, with AI-powered screening ensuring smarter, faster hiring decisions. Interactive AI screening interview powered by Google Gemini saves time and energy for both the recruiter and the job poster, and makes the hiring process much less nerve wrecking. Recruiter can access meeting notes at any time.

What it does

Swiped-In simplifies job searching and hiring through an intuitive swipe-based system:

Job Seekers: Upload resumes, add key details, and swipe through job listings.
Recruiters: Swipe through potential candidates, filtering the best fits.
Matching System: A match occurs when both parties swipe right.
AI-Powered Screening: The recruiter's AI agent powered by Google Gemini AI conducts initial interviews to streamline the hiring process.
Additional Features: Chat management, application tracking, and personalized job/candidate recommendations.

How we built it

We built Swiped-In using:

Frontend: Next.js for a smooth, cross-platform user experience.
Backend: Node.js with Express for handling API requests.
Database: MongoDB Atlas for efficient data management.
AI Integration: A generative AI recruiter agent with a 3d avatar to conduct initial screening interviews.
Authentication: Secure user login for job seekers and recruiters.
Hosting: Deployed on cloud infrastructure for scalability.

Challenges we ran into

Time Constraints: Building a fully functional job-matching platform in under 18 hours was a major challenge.
AI Screening Integration: Training and fine-tuning the AI recruiter agent to ask relevant screening questions.
Matching Logic: Ensuring job seekers and recruiters had meaningful matches based on relevant criteria.
MongoDB Integration: Designing the database architecture to efficiently manage job postings, candidate profiles, resumes, matches, AI screening responses, and chat interactions.

Accomplishments that we're proud of

Built a working prototype in less than 18 hours 🚀
Successfully integrated AI-powered recruiter screening interviews
Created an intuitive swipe-based job-matching experience
Used MongoDB Atlas efficiently for real-time data handling
Secured the domain https://www.swipedin.co for future expansion
Aligned with multiple hackathon challenges, making Swiped-In one of the most ambitious projects at the event!

What we learned

AI in Hiring: How AI can streamline recruitment and improve hiring efficiency.
Scalability with MongoDB Atlas: Leveraging cloud databases for handling large datasets.
User Experience Design: Creating an intuitive, gamified job-matching system.
Team Collaboration Under Pressure: Working together to build a complex project within a limited timeframe.

What's next for Swiped-In

We see huge potential for Swiped-In beyond this hackathon:

Enhancing AI Screening: Making AI recruiter agents more sophisticated for deeper interview analysis.
Expanding Job & Candidate Matching Criteria: Smarter filters based on skills, experience, and preferences.
Improving Application Tracking & Insights: Helping job seekers make informed career moves.
Monetization Strategies: Premium job postings and recruiter subscriptions.
Launch on App Stores: Bringing Swiped-In to a wider audience.

Despite the intense 48-hour coding sprint, our team is incredibly proud of what we have built. Swiped-In has the potential to redefine hiring, and this is just the beginning! 🚀