CareerOs

CareerOS: Your AI-powered career twin that analyzes skills, identifies gaps, and builds a personalized roadmap to career readiness.

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Inspiration

Choosing a career in technology can be overwhelming for students because they often do not know which skills they are missing, how prepared they are for a specific role, or what they should learn next. We wanted to build something that goes beyond a simple career recommendation and instead gives students a personalized view of their current career readiness.

What it does

CareerOS is an AI-powered career guidance platform that creates a personalized Career Twin for each user. It analyzes different aspects of a student's profile, including resume strength, technical skills, interview performance, skill gaps, projects, and coding practice.

The platform provides:

  • Personalized career-role recommendations
  • Career readiness and skill-gap analysis
  • Resume/ATS analysis
  • AI mock interview practice
  • LeetCode/Algo Arena tracking
  • Personalized learning roadmaps
  • Career growth simulation
  • Salary and company-fit projections
  • AI mentor-style career guidance

How we built it

We built CareerOS as a modern web application with a React and TypeScript frontend. Supabase is used for authentication and storing user-related career data. The platform connects different modules such as Resume Analysis, AI Mock Interviews, Skill Gap Analysis, Algo Arena, and Career Twin into a unified dashboard.

The Career Twin combines information from these modules to create a dynamic picture of the user's career readiness and identify the areas that can have the greatest impact on their growth.

Challenges we faced

One of the biggest challenges was keeping data synchronized across multiple modules. A user's resume score, interview performance, coding progress, and selected career role all need to contribute to a consistent career profile.

We also had to handle API integrations, authentication, database synchronization, and different states where information may not yet be available.

What we learned

Through this project, we learned how to build and connect multiple application modules into one platform, work with Supabase authentication and databases, integrate external APIs, and design a personalized recommendation experience. Most importantly, we learned that useful career guidance should not only tell users what career to choose, but also clearly explain what they should do next

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