CareerLens

Inspiration

Choosing a career is often difficult because students and early-career professionals may know what they want to become, but not what skills they are missing or what they should learn next.

We built CareerLens to turn career planning into a personalized, actionable process. Instead of only analyzing a resume, CareerLens helps users understand where they currently stand, identify skill gaps, create a learning path, and practice for interviews.

What it does

CareerLens is an AI-powered career guidance platform that helps users:

Analyze their resume and existing skills
Identify gaps between their current profile and a target career
Generate personalized learning roadmaps
Get AI-powered career guidance and mentoring
Prepare for interviews with mock interview practice
Track their progress toward their career goals

The goal is to connect "Where am I now?" with "What should I do next?"

How we built it

CareerLens is built as a full-stack web application with a modern frontend, backend API, database layer, and AI-powered features.

The frontend provides the user experience for resume analysis, career planning, learning roadmaps, progress tracking, and interview preparation.

The backend handles application logic, APIs, authentication/data flow, and communication with the AI services and database.

AI is used to transform a user's resume, skills, goals, and interview responses into personalized career insights and recommendations.

The system is designed as an end-to-end workflow rather than a collection of isolated AI features: user data goes in, the platform analyzes it, generates recommendations, and presents concrete next steps.

Challenges we faced

One of the biggest challenges was making AI-generated career advice useful rather than generic. Career guidance needs to consider the user's existing skills, target role, experience, and goals.

We also had to connect multiple parts of the system—resume processing, AI analysis, personalized recommendations, user progress, and interview preparation—into a single consistent experience.

Another challenge was balancing a feature-rich product with a simple interface so that users can understand what to do next without being overwhelmed.

What we learned

Building CareerLens taught us that an AI application is not just about calling an LLM. The quality of the product depends on how user data is structured, how prompts and workflows are designed, how the output is validated and presented, and how the different parts of the application work together.

We also learned a lot about building an end-to-end full-stack AI product and turning an open-ended problem like career planning into actionable user workflows.

What's next

We see CareerLens evolving into a more comprehensive career companion with deeper job-market insights, stronger interview evaluation, more adaptive learning plans, and better personalization based on a user's long-term progress.