Inspiration

What it does

Inspiration The idea for this project came from a simple but recurring problem we kept noticing: people don’t fail to get jobs because they lack effort or intelligence — they fail because they don’t know what they’re missing.

Across students, career switchers, and even laid‑off professionals, the same pattern appeared. Everyone was working hard, but in different, unstructured directions. CVs were either generic or misleading, and most advice online focused on writing better résumés rather than becoming the candidate companies actually hire. We wanted to build something that replaces guesswork with clarity.

What We Built We built a prototype platform that helps users move from their current qualifications to their target role in a structured, transparent way.

The system works by:

Taking a user’s existing CV and their target role or company

Analyzing real employee profiles already hired for that role

Generating a target CV that represents what strong candidates actually look like

Breaking the gap into clear, actionable tasks such as skills, certifications, projects, or experience

Tracking progress visually until the CV is ready to apply

Instead of asking users to blindly edit their résumé, the platform shows them why something is missing and what needs to be done to close that gap.

How We Built It The project was designed as an AI‑driven system with no traditional databases, relying instead on structured file handling and external APIs.

Key components include:

An LLM‑driven onboarding flow to understand user goals and constraints

Resume analysis and comparison using real employee data

A Notion‑like integrated CV editor with inline feedback and locked sections

A dynamic roadmap that adapts based on the user’s progress

A context‑aware chatbot to clarify tasks, suggest alternatives, and guide decisions

Mathematically, the platform evaluates progress as a similarity between the current CV state and the target CV:

Progress

Completed Requirements Total Target Requirements Progress= Total Target Requirements Completed Requirements ​

This allows progress to be tracked transparently rather than subjectively.

Challenges We Faced One of the biggest challenges was avoiding oversimplification. Career paths are not linear, and forcing users into rigid categories would reduce trust in the system. Designing a roadmap that adapts dynamically without overwhelming the user required careful prompt design and prioritization logic.

Another challenge was balancing automation with control. While the AI generates most of the CV content, users still need the freedom to edit and understand what’s happening — otherwise the output feels artificial. Designing an editor that supports both guidance and ownership was a key learning.

What We Learned Through this project, we learned that:

Clarity is more valuable than motivation

Users trust systems that explain why, not just what

Career tools work best when they feel like execution engines, not content platforms

Most importantly, we learned that helping someone get a job isn’t about writing a better CV — it’s about making the gap visible and giving them a realistic way to close it.

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for PathForge

Built With

• api
• gemini
• taskscript