ATSPass

Input page where users can upload their resume and job description.
Analysis page where users can see their resume match score, recommendations, gap analysis, and missing keywords.

Inspiration

Navigating Applicant Tracking Systems (ATS) can be frustrating and time-consuming for job seekers. We wanted to create a simple, user-friendly solution that seamlessly integrates job description keywords into resumes without altering their meaning or structure. By automating this process, ATSPass helps users optimize their resumes efficiently, saving them the hassle of manually tailoring their applications for each job.

What it does

ATSPass analyzes the user’s resume and a given job description, identifies key terms, and strategically injects them into the resume in a natural, non-disruptive way. The goal is to improve ATS compatibility without compromising readability or accuracy, ensuring that users can pass automated screenings without unnecessary rewriting.

How we built it

ATSPass is built using a modern Python-based tech stack that combines Streamlit for the frontend interface, custom NLP modules for resume analysis and enhancement, and Flask for API endpoints. It uses spaCy, to extract and analyze context of the key terms from resumes and job descriptions. scikit-learn handles text similarity and keyword matching, ensuring precise term placement and LLM handles the replacement.

Challenges we ran into

Integrating the front end with the backend proved challenging, particularly in ensuring the expected output while working under tight time constraints. Additionally, developing robust test cases for resume modifications was difficult within the available timeframe. Formatting the modified resume properly on the front end was another hurdle we had to overcome.

Accomplishments that we're proud of

We’re proud of implementing a keyword integration system that works independently of large language models (LLMs). By leveraging structured text processing and keyword matching, we successfully enhanced resumes without excessive rewriting, preserving their original intent and readability.

What we learned

We learned that collaboration is vital throughout development. We learned the importance of working on the same branch, regularly syncing changes, and ensuring smooth reintegration of ideas.

What's next for ATSPass

Next, we plan to introduce resume formatting templates, allowing users to receive their optimized resumes in a professionally structured format of their choice rather than plain text output. This will further enhance usability and ensure resumes are job-ready