VetriPath

Inspiration The inspiration behind VetriPath came from our own struggles and those of our peers in preparing for technical and behavioral interviews. While there are many resources online, most are static, generic, and don’t provide actionable feedback. We wanted to create something more dynamic and personalized—an experience that feels like having a real interview coach available 24/7. With recent advancements in AI, especially in natural language understanding and generation, we saw an opportunity to build a tool that could simulate real interview scenarios, help users identify their blind spots, and grow in confidence. VetriPath was born from a desire to democratize high-quality interview preparation and make it accessible to anyone, anywhere.

What it does VetriPath is an AI-powered mock interview platform that enables users to practice personalized interview questions tailored to their target roles. By pasting a job description and selecting preferences such as interview type and experience level, users receive role-specific questions generated by AI. After each session, VetriPath provides detailed, actionable feedback—highlighting strengths, identifying areas for improvement, and guiding users toward interview readiness. It empowers job seekers to build confidence and prepare effectively in a realistic interview-like environment.

How we built it We built VetriPath using bolt.new, which allowed rapid prototyping and real-time collaboration. The frontend is developed using React.js and Tailwind CSS for a clean, responsive UI. The AI capabilities are powered by OpenAI’s GPT API, enabling dynamic question generation and insightful feedback. We used Firebase for session management, user authentication, and data storage. The entire system is designed to be modular and scalable, with a focus on delivering smooth, human-like interview interaction.

Challenges we ran into One of the main challenges was ensuring the AI-generated questions were relevant, context-aware, and role-specific. Fine-tuning prompts to maintain a balance between technical depth and interview realism required multiple iterations. We also faced issues with rendering dynamic feedback components within the practice session without interrupting the user flow. Managing session persistence and seamless resume functionality across devices was another technical hurdle we worked hard to overcome.

Accomplishments that we’re proud of We’re proud to have built a fully functional SaaS platform within a limited timeframe that simulates real interview experiences with AI at its core. From question generation to feedback analytics, every component works together cohesively. Achieving a balance of usability, depth, and real-world utility while keeping the UI clean and intuitive is something we’re especially proud of.

What we learned This project deepened our understanding of prompt engineering for career-based use cases, as well as advanced frontend state management in React. We learned the importance of seamless user experience when dealing with async AI responses and how to structure scalable SaaS features under time pressure. Most importantly, we learned how impactful AI can be in transforming traditional learning processes into something smarter and more accessible.

What’s next for VetriPath Next, we plan to integrate voice-based mock interview simulations and expand the feedback system with performance scoring and growth tracking over time. We’re also exploring integrations with platforms like LinkedIn and job boards to auto-fetch job descriptions and make practice sessions even more relevant. Our long-term goal is to make VetriPath a go-to personal AI coach for every job seeker preparing for interviews.

Built With

• openai
• react
• supabase
• typescript
• web