InterviewPilot AI

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  InterviewPilot AI architecture and Google Cloud flow

Inspiration

Many students prepare for interviews using the same fixed set of questions, even though every candidate has different strengths, weaknesses, experience, and target roles. We wanted to build an AI coach that actually adapts to the learner instead of giving the same advice to everyone.

What it does

Interview Pilot AI is an adaptive interview-preparation agent. It first understands the candidate's target company, role, experience level, and difficult topics. It then conducts personalized mock interviews, evaluates responses, identifies weak areas, and automatically adjusts the next questions and preparation plan.

How we built it

We designed the system as an agent workflow using Gemini and Google technologies. The agent maintains the candidate's context, generates role-specific questions, evaluates answers, tracks performance, and creates an evolving preparation plan. Google Cloud services are used to support the application's deployment and data handling.

Challenges

The main challenge was making the system genuinely adaptive rather than a simple chatbot. We needed the agent to remember previous performance, recognize weak areas, choose appropriate question difficulty, and change the learning plan based on new results. We also focused on making the interaction simple and useful for students.

What we learned

We learned how agent AI can go beyond simple question-and-answer interactions by combining reasoning, memory, evaluation, and dynamic planning. Building Interview Pilot AI also helped us understand how Gemini and Google Cloud can be combined to create practical AI agents that continuously adapt to users.

Future improvements

We plan to add voice-based interviews, resume analysis, company-specific interview research, interview analytics, and multi-agent collaboration for technical, behavioral, and HR preparation.

Built With

• ai
• cloud-run
• firestore
• gemini
• google
• google-adk