Architecture & Performance Benchmark of My Mock Interview – AI Tailored Interview Platform
- 7‑agent LLM pipeline orchestrates JD parsing, resume gap analysis, question generation, and rubric evaluation in a single interview session.
- FastAPI + React 19 + PostgreSQL 16 + MinIO S3 deliver sub‑second API latency and real‑time feedback.
- Production topology uses Nginx for dual static/SPA serving, automatic model failover, and zero‑JS SEO landing pages.
- Live demo: https://interview.nevatal.id
The Engineering Challenge: Why My Mock Interview – AI Tailored Interview Platform Was Architected
Traditional mock interviews rely on generic question banks and human evaluators, creating a mismatch between a candidate’s actual experience and the role’s specific demands. My Mock Interview solves this by automating a fully personalized interview experience that analyzes a candidate’s resume against a target job description, identifies critical gaps, and delivers real‑time, rubric‑driven scoring. The platform must handle complex multi‑agent workflows, serve a responsive web UI, and guarantee high availability with sub‑second response times for a frictionless user journey.
Core Architecture & Technical Stack Deep-Dive
Containerized Micro‑Service Stack (Docker Compose)
┌───────────────────────┐
│ User Web Browser │
└───────────┬───────────┘
│ Port 3000
▼
┌───────────────────────┐
│ Nginx Proxy │
└─────┬───────────┬─────┘
│ │
▼ ▼
┌───────────────────┐ ┌───────────────────────────────────┐
│ FastAPI Backend │ │ • / -> Zero‑JS Static Landing │
│ Port 8000 │ │ • /app/ -> React 19 SPA Bundle │
└─────────┬─────────┘ └───────────────────────────────────┘
│
┌───────┼───────┐
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌───────────────────┐
│PostgreSQL│ │ MinIO │ │ OpenRouter API │
│ (5432) │ │(9000/9001│ │ (Cloud Inference)│
└──────────┘ └──────────┘ └───────────────────┘
- FastAPI (Python 3.12) – Asynchronous, OpenAPI‑first API layer using SQLAlchemy 2.0 + asyncpg for non‑blocking database operations.
- React 19 / Vite SPA – Lightning‑fast client with automatic code splitting and hot module replacement.
- PostgreSQL 16 – Relational store for interviews, JDs, resumes, and performance metrics.
- MinIO S3 Storage – Production‑grade object storage for PDF resumes, session snapshots, and evaluation dossiers.
- OpenRouter Multi‑Model Inference – Failover‑aware LLM gateway that balances cost and performance across free and premium models.
7‑Agent LLM Pipeline
- JD Parser Agent – Extracts keywords, required skills, and role expectations from the uploaded job description.
- Resume Analyzer Agent – Performs NLP‑driven parsing of the candidate’s resume, mapping experience to technical and behavioral competencies.
- Gap Analysis Agent – Computes matched proficiencies, missing competencies, and high‑priority interview focus areas.
- Spec Builder Agent – Synthesizes a unified Interview Specification contract defining question categories and difficulty tiers.
- Question Generator Agent – Produces a shuffled, personalized question sequence covering technical depth, system design, behavioral STAR, and gap‑probing prompts.
- Answer Evaluator Agent – Scores each response on Technical Depth (0‑10) and Communication Clarity (0‑10) with actionable feedback.
- Final Reviewer Agent – Consolidates turn‑by‑turn scores into an executive scorecard with composite 0‑100 rating and hiring recommendation.
Key Features Breakdown & Practical Benefits
Multi‑Mode Interview Support
Engineered for both In‑Depth Technical Engineering and HR Behavioral modes, the platform seamlessly switches between LeetCode‑style algorithmic challenges and STAR‑based behavioral scenarios.
Real‑Time Rubric Evaluation
Every answer is instantly graded using a dual‑criterion rubric. Users receive immediate, constructive feedback that drives rapid improvement.
WebMCP Architecture Endpoints
Autonomous agents can initiate interviews and retrieve evaluations via standardized WebMCP API contracts, enabling future extensibility and integration with external tools.
Automatic Failover & Session Resilience
Built‑in free‑model failover prevents session drops when credits are exhausted, ensuring uninterrupted practice sessions.
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Executive Scorecard & Hiring Recommendation
Post‑interview reports aggregate scores, competency breakdowns, strengths, growth areas, and a data‑driven hiring verdict—all exportable as PDF dossiers.
Real‑World Deployment & Production Topology
The platform operates in a Docker Compose environment, with Nginx handling both SEO‑optimized static landing pages and the React SPA. This dual‑serving strategy ensures crawlability while delivering a dynamic, interactive interview experience. The backend leverages async PostgreSQL for high‑throughput data persistence, while MinIO provides scalable object storage for large resume files and session snapshots.
Data Flow & How It Works: Step‑by‑Step Workflow
1. Upload & Ingest – Candidate submits target JD (text/PDF) and uploads resume (PDF/text). Files are validated, stored in MinIO, and metadata saved to PostgreSQL.
2. Agent Orchestration – FastAPI triggers the 7‑agent pipeline sequentially, passing context (JD vector embeddings, resume embeddings) via message queues.
Points, and communication clarity scores.
5. Answer Evaluation – The Answer Evaluator Agent grades each response against the dynamic rubric, returning a JSON payload with scores and feedback.
6. Final Review – The Final Reviewer Agent aggregates turn‑by‑turn scores, computes an overall composite rating, and generates the executive scorecard PDF.
7. Reporting – Candidates view real‑time progress in the interview terminal and download the final report with actionable insights.
Architectural Comparison: My Mock Interview vs Traditional Approaches
| Aspect | Traditional Mock Interview | My Mock Interview – AI Tailored Interview Platform |
|---|---|---|
| Question Source | Static, generic bank | Dynamic, gap‑driven generation |
| Evaluation Method | Human subjectivity | Objective rubric scoring (0‑10) |
| Personalization | Minimal | Full resume‑JD alignment analysis |
| Availability | Schedule dependent | 24/7, automated pipeline |
| Scalability | Human‑limited | Containerized micro‑services with auto‑failover |
Frequently Asked Questions (FAQ)
What technologies power the backend API?
The backend runs on FastAPI (Python 3.12) with SQLAlchemy 2.0 and asyncpg, deployed in Docker Compose alongside PostgreSQL 16, MinIO S3, and Nginx.
How does the 7‑agent pipeline ensure interview relevance?
Each agent focuses on a distinct stage—from parsing the JD and analyzing the resume to gap analysis, spec building, and targeted question generation—ensuring the interview directly addresses the candidate’s unique strengths and weaknesses.
Can I access the platform on mobile devices?
Yes. The React 19 SPA is fully responsive, providing a seamless interview experience across smartphones, tablets, and desktops.