Building an Agentic Job Search Platform: The Journey Behind RepediaAI

#ai #automation #career #productivity

As a fresh graduate in 2022, I found myself in the middle of what felt like the most brutal job market in recent history. COVID had turned everything upside down, layoffs were happening left and right, and despite sending out 800 applications over several months, I only landed 4 interviews and 1 offer. That's a 0.5% interview rate and 0.125% success rate – pretty soul-crushing numbers for any developer.

But here's what really got me thinking: I recently saw a Google software engineer position that received 4,000 applications on LinkedIn. Yet my friends at Google tell me they're still struggling to find great candidates. There's clearly a massive matching problem in our industry.

That's when I decided to build RepediaAI – an AI-powered platform that solves the job search problem from both sides using modern vector matching algorithms.

The Technical Architecture

Vector-Based Skills Matching

The core of RepediaAI is built on vector embeddings that transform both job requirements and candidate skills into high-dimensional vector spaces. Here's how it works:

Job Description Processing: We parse job descriptions and extract key requirements, technologies, and skills
Resume Vectorization: Candidate resumes are similarly processed and converted into skill vectors
Semantic Matching: Using cosine similarity and other distance metrics, we match candidates to jobs based on semantic understanding, not just keyword matching

This approach means that a candidate with React experience will still match well with a job requiring "modern JavaScript frameworks," even if React isn't explicitly mentioned.

AI Interview System

One of our most technically challenging features is the AI interviewer. We've built a conversational AI that can:

Generate company-specific interview questions based on real interview data
Conduct voice-based interviews with natural conversation flow
Provide real-time feedback on responses
Adapt difficulty based on the candidate's performance

The system uses large language models fine-tuned on interview scenarios, combined with speech-to-text and text-to-speech capabilities for a seamless experience.

Automated Application Pipeline

We've integrated with major job platforms to enable one-click applications to 100+ jobs. The technical challenges here included:

Web scraping and API integrations with job boards
Form auto-filling across different website structures
Email monitoring for application status tracking
Rate limiting and anti-bot detection circumvention

Real-World Impact

Since launching our beta, we've seen some encouraging metrics:

Average time to find relevant jobs reduced from hours to minutes
Interview preparation scores improved by 40% after using our AI interviewer
Application success rates increased 3x compared to traditional job boards

What's Next

We're currently expanding the platform to serve recruiters with an AI-powered candidate search system. Recruiters will be able to say "Find me 20 backend engineers proficient in Java and cloud" and get instant, ranked results.

We're also building out features like:

Real-time interview feedback during actual interviews
Advanced resume optimization
Community features for sharing interview experiences

Join Our Beta

We're actively looking for developers to try RepediaAI and provide feedback. Right now, we're offering free access to our core features and discounted premium plans for early users.

The job search process is broken, but with the right technology, we can fix it. If you've ever felt the frustration of sending hundreds of applications into the void, I'd love to hear your thoughts and have you try what we've built.

Visit https://www.repediaai.com/ to get started.

RepediaAI is currently in beta. Try it out and let us know what you think – your feedback directly shapes our product roadmap.