🤖 VeriAI — AI-Powered Resume-Based Skill Verification System

🚀 Overview

VeriAI revolutionizes the hiring process by generating smart AI-based tests directly from resumes.
Candidates upload their resume, and VeriAI (powered by Gemini API) creates a personalized skill test.
After successful completion, candidates receive a unique verification code valid for 40 days —
recruiters can verify this code publicly without needing to log in.

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🧠 Problem

Recruiters face issues like:

• Fake or exaggerated resumes.
• Time-consuming manual evaluations.
• No quick way to verify real skills.

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💡 Solution

VeriAI automatically:

1. Parses the resume using Gemini AI → extracts skills and experience.
2. Generates a personalized test (20 basic, 10 advanced, 5 logical questions).
3. Scores and evaluates performance.
4. Issues a verification ID valid for 40 days.
5. Allows recruiters to verify ID on a public page instantly.

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⚙️ Tech Stack

Layer	Technology	Description
Frontend	Next.js 15 (App Router)	SEO optimized, modern React framework
Styling	Tailwind CSS + ShadCN/UI	Responsive, minimal UI components
Backend	Next.js API Routes	Serverless, lightweight, integrated backend
Database	MongoDB Atlas	Stores users, tests, and verification records
AI Engine	Google Gemini API	Resume parsing + test generation
File Handling	Cloudinary / S3	For storing resumes securely
Auth	NextAuth.js	Secure login for candidates
Deployment	Vercel	Fast, global CDN + serverless APIs

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🧩 Core Features

• 🧾 Smart Resume Parsing → Extracts skills & domains automatically.
• 🤖 AI-Generated Tests → Gemini creates personalized skill-based questions.
• 🧑‍💻 Real-Time Test UI → 35–40 minute adaptive assessment.
• 🔐 Unique Verification ID → Auto-generated after passing score (≥80).
• 🌐 Public Verify Page → Recruiters can verify authenticity without login.
• 🧠 AI Feedback → Suggests skills to improve after test.
• ⏳ Expiry System → Codes expire after 40 days.

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🧱 Folder Structure

veriai/
 ┣ app/ → pages and APIs
 ┣ components/ → UI components
 ┣ lib/ → integrations (Gemini, MongoDB, utils)
 ┣ models/ → MongoDB schemas
 ┣ public/ → static assets
 ┣ styles/ → Tailwind styles
 ┣ .env.local → secrets
 ┗ README.md

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🔑 Environment Variables

Key	Description
GEMINI_API_KEY	Your Google Gemini API key
MONGO_URI	MongoDB connection string
NEXTAUTH_SECRET	Secret for authentication
NEXT_PUBLIC_BASE_URL	Base URL for frontend

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🧠 API Routes

Route	Description
/api/parse	Parse uploaded resume and extract skills
/api/generateTest	Generate AI test using Gemini
/api/submitTest	Submit test and calculate score
/api/verifyCode	Verify code validity and details

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🚀 Deployment

Deploy on Vercel:

vercel

Connect your MongoDB Atlas & Gemini API before deploying.

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🧭 Future Roadmap

• 📸 Face detection for anti-cheating (proctoring)
• 🔐 Blockchain-based verification for permanent records
• 📊 Recruiter dashboard & analytics
• 🧩 Gamified skill progress tracking

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💬 Example Workflow

1️⃣ Candidate uploads resume
2️⃣ VeriAI parses it → finds “Frontend Developer”
3️⃣ AI generates test → React, JS, CSS questions
4️⃣ Candidate scores 86/100
5️⃣ System issues VERI-AI-XA2KZ3 (valid 40 days)
6️⃣ Recruiter visits /verify → enters code → sees results instantly.

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👨‍💻 Author

Ravi Kumar Pandey
BTech CSE | MERN + AI Developer | Forangex
📍 Nashik, India
🌐 https://raviverse.dev/

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🧡 License

MIT License © 2025 VeriAI