Applying for jobs can feel like rewriting the same story for every role. I wanted to make that process easier for students without making up qualifications or taking control away from the person applying.
What it does
AutoApply AI helps you move from a job listing to a reviewed application packet. Search supported job sources, read the listing, and compare its requirements with your resume. The app highlights matching and missing terms, then helps create a tailored resume and cover letter you can download as PDFs. It also offers interview preparation and an application tracker. You review your materials and submit applications yourself.
Its ATS-style score is a guide to keyword coverage, not a hiring prediction. Suggestions should stay grounded in the experience you provide.
How I built it
I built the app with Python and Streamlit. It includes Google sign-in through OpenID Connect, resume and job-description analysis, and PDF exports. When generating AI-assisted drafts, the app asks for consent before sending resume and job-description content to its configured AI provider. Guest access is also available for trying the demo.
Challenges
The hardest part was making the output useful without overstating what an ATS score means. Resumes and job descriptions use different wording for similar skills, so I focused on showing the match clearly and keeping the candidate in control of edits. Configuring Google sign-in for the hosted app was another challenge because local and deployed apps use different callback URLs and settings.
What I learned
I learned that career tools need to be transparent as well as helpful. A score should explain what it measures, and AI suggestions should support a candidate’s real experience rather than invent a stronger one. I also learned more about connecting authentication and deployment settings for a cloud-hosted app.
What’s next
I’d like to improve job-source coverage, keep refining resume parsing, and use feedback from students to make each step clearer.
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