Tech Stack

🧠 InView

🚀 About the Project

InView is an intelligent interview preparation and feedback platform designed to personalize the hiring journey for candidates.

Inspired by the frustration of generic rejection emails and one-size-fits-all interviews, we built InView to help users grow through smart feedback, tailored mock interviews, and AI-driven insights.

We envisioned a tool that doesn't just simulate interviews, but makes them smarter, contextual, and genuinely helpful — all powered by AI.

💡 What Inspired Us

Job interviews are tough — not just because of the questions, but because of how impersonal and opaque the whole process is. Candidates often don’t know what went wrong.

We asked:

“What if an AI could bridge that gap — prepare you for your dream role, track your progress, and give you real feedback?”

This idea inspired us to build InView — a place where candidates could benefit from a transparent, data-driven, and personalized interview process.

🛠️ How We Built It

🖥️ Frontend:

React + Material UI
Clean, accessible UI with domain selection, resume upload, and feedback views
Context-aware form autofill using parsed resume data
Gemini LLM-integrated chat interface for mock interviews

🔧 Backend:

Node.js + Express.js + FastAPI
MongoDB for flexible data storage
pdf-parse to extract structured data from resumes

🤖 AI Integration:

Gemini API
- Generates domain-relevant interview questions
- Provides personalized post-interview feedback
- Maintains session context for better responses

🧠 What We Learned

Handling dynamic resume formats and extracting consistent data
Architecting a user flow that feels intuitive, not overwhelming
Designing scalable MongoDB schemas to support evolving user profiles
Prompt engineering for interview realism and tailored AI responses
Building feedback loops that empower users with actionable insights

⚔️ Challenges We Faced

Uploading custom job descriptions for hyper-specific prep
Securely handling sensitive user information
Getting LLMs to stay on-topic and simulate realistic interviewers
Aligning user context across resume data, job descriptions, and chat feedback
Designing a UI that balances simplicity and power

🌱 What's Next

Voice-based interview simulations
Analytics dashboard for skill progression tracking
More granular feedback (using rubrics like STAR method or role-specific metrics)
Integrating recruiter mode for evaluating candidates with context-aware scoring

👥 Team

Eric Somogyi
Salvador Ortiz
Mohammed Saalim K
Derick Johnson

💬 Final Thoughts

InView isn’t just a tool — it’s a mentor, a prep buddy, and a smart mirror that helps you grow. With AI, we’re reimagining what interview prep and feedback can look like.