AI Interview Platform Blueprint and Architecture
The AI Interview Platform Blueprint outlines a full-stack system designed for automated candidate screening. It leverages Next.js for the frontend, Firebase for persistence and authentication, and specialized APIs like Vapi and Stream.io for real-time voice and video. The core functionality involves an adaptive AI agent conducting interviews and a GenAI model analyzing transcripts to generate detailed feedback reports.
Key Takeaways
The platform uses Vapi for adaptive voice Q&A and Stream.io for video integration.
Firebase handles user authentication and stores interview reports securely in Firestore.
GenAI models analyze interview transcripts to provide detailed feedback on skills and communication.
The user flow moves from secure onboarding to real-time interview, then to report generation.
What core technologies power the AI Interview Platform?
The platform relies on a robust, modern technology stack to handle real-time interaction and data persistence. Next.js provides a fast, scalable frontend experience, while Firebase manages user authentication and data storage using Firestore. Specialized services like Vapi enable real-time voice interactions, and Stream.io handles video integration, ensuring a seamless, interactive interview environment. The final component is a GenAI API, which serves as the crucial feedback engine for evaluation.
- Frontend: Next.js
- Persistence & Auth: Firebase (Firestore & Auth)
- Realtime Voice: Vapi
- Video Integration: Stream.io
- Feedback Engine: GenAI API
How does the user interaction sequence flow during an AI interview?
The user interaction sequence begins with secure onboarding, where candidates log in via Firebase Auth and receive an AI greeting from Vapi. The core interview session then initiates a video call using Stream.io, featuring adaptive, voice-based Q&A driven by the AI agent. Following the session, the system records and transcribes the interaction. This transcript is sent to the GenAI model for evaluation, resulting in a detailed report saved to Firestore, which the user can access via the dashboard.
- Onboarding: Login (Firebase Auth) and AI Greeting & Data Collection (Vapi).
- Interview Session: Stream.io video call, adaptive Q&A via voice, and recording/transcription.
- Evaluation & Feedback: Transcript sent to GenAI for report generation (Strengths, Weaknesses, Tips).
- Dashboard Access: View history and schedule new sessions.
What are the distinct roles of the AI agents in the interview process?
The AI logic is divided into two primary functions: interviewing and evaluation. The Vapi Agent acts as the interviewer, responsible for gathering initial candidate profile data and generating adaptive questions based on the candidate's responses and profile. The GenAI Model serves as the evaluator, analyzing the transcribed interview data. This model specifically assesses technical knowledge demonstrated during the conversation and evaluates soft skills, such as communication clarity and confidence.
- Vapi Agent (Interviewer): Gathers candidate profile data and generates adaptive questions.
- GenAI Model (Evaluator): Analyzes technical knowledge and assesses communication clarity & confidence.
Who are the primary participants and what are their roles in the platform?
The platform defines two key user journey roles: the Candidate and the AI Agent. The Candidate is the primary user who participates directly in the voice and video interview session. Their main outcome is receiving a personalized feedback report detailing their performance. The AI Agent, acting as the interviewer, handles the session initiation, provides the greeting, and drives the dynamic question-and-answer process to ensure a structured and adaptive assessment experience.
- Candidate: Participates in Voice/Video Interview and receives Personalized Feedback Report.
- AI Agent (Interviewer): Handles greeting and session initiation, and drives dynamic Q&A.
How is the overall system architecture structured?
The system architecture is layered, starting with the Frontend Layer built on Next.js, which manages authentication, the user dashboard, and the overall user interface. The core functionality relies on Cloud Services, primarily Firebase for secure authentication and data persistence (Firestore), alongside Vapi and Stream.io for handling real-time media streams. Optional services, such as Cloud Functions, can be used for triggers, and Firebase Storage can provide media backup for recordings, enhancing system robustness and scalability.
- Frontend Layer: Next.js (Auth, Dashboard, UI).
- Cloud Services Core: Firebase (Auth & Firestore) and Vapi & Stream.io (Realtime Media).
- Optional Services: Cloud Functions (Triggers) and Firebase Storage (Media Backup).
What is the sequence of data movement within the platform?
Data flow begins when a user successfully logs in, resulting in an authenticated session created via Firebase. During the interview, interaction data, including voice and video streams, is captured by Vapi and Stream.io. This raw data is then converted into a transcript and processed by the GenAI model for evaluation and scoring. Finally, the resulting detailed report is stored in Firestore and subsequently displayed to the user through the Next.js dashboard interface.
- Auth Session Created (Firebase).
- Interaction Data Captured (Vapi/Stream.io).
- Transcript Processed (GenAI).
- Report Stored & Displayed (Firestore -> Next.js).
What potential features could enhance the AI Interview Platform?
Future development focuses on increasing analytical depth and integration capabilities. Potential enhancements include introducing a Hybrid Interview Mode, allowing for combined AI and human interaction. Advanced AI analysis could incorporate tone and confidence assessment. Furthermore, adapting questions based on uploaded resumes and integrating external profiles like LinkedIn or GitHub would personalize the experience. Finally, adding a Coding Test Leaderboard would expand the platform's utility for technical roles.
- Hybrid Interview Mode (AI + Human).
- AI Tone/Confidence Analysis.
- Resume-Based Question Adaptation.
- External Profile Integration (LinkedIn/GitHub).
- Coding Test Leaderboard.
Frequently Asked Questions
Which components handle real-time voice and video in the platform?
Vapi is utilized for managing the real-time voice interactions and adaptive Q&A driven by the AI agent. Stream.io is integrated specifically to handle the video call initiation and streaming during the interview session.
How is candidate feedback generated and stored?
The interview transcript is sent to a GenAI API for analysis. This model generates a detailed report covering strengths, weaknesses, and tips. The final report is then securely saved to Firebase Firestore for user access.
What is the primary function of the Vapi Agent?
The Vapi Agent serves as the AI interviewer. Its primary functions are to greet the candidate, collect initial profile data, and dynamically generate adaptive questions throughout the session based on the candidate's responses.
What role does Next.js play in the system architecture?
Next.js forms the Frontend Layer of the platform. It is responsible for managing the user interface, handling user authentication flows, and displaying the dashboard where users can view their interview history and reports.
How does the platform ensure secure user access?
Secure user access and persistence are managed by Firebase. Specifically, Firebase Auth handles the login and authentication process, while Firestore is used to securely store user data and generated interview reports.
