Welcome to the Certifiable Inc. Certification System repository!

This project is designed to provide AI-Powered Certification System which automates time-intensive grading of exams, managing accuracy and feedback, and maintaining the integrity of tests and case studies, handling customer inquiries on products, orders, returns and refunds while seamlessly integrating with our company’s database. This AI-driven solution is designed to streamline grading process through advanced generative AI, maintaining the credibility, accuracy, and scalability of certification process at best.

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🔹 Overview

Certifiable Inc. is transforming its software architecture certification process with AI-driven automation, cloud scalability, and real-time candidate support. This project was designed to tackle scalability, candidate experience, administrative inefficiencies, and cost concerns, ensuring faster, fairer, and more efficient certifications.

🎥 Demo Video

▶ Watch the video on Vimeo

📌 Table of Contents

1. Problem Statement
2. Team Members
3. Proposed Solution
4. Challenges & Impact
5. System Architecture
6. Technical Implementation
7. Architecture Decision Records (ADRs)
8. How to Contribute

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🎯 Problem Statement

Certifiable Inc. faced several challenges in scaling its certification process:

• Manual grading inefficiencies slowed test evaluations.
• Candidate experience issues due to strict deadlines & delays.
• Scalability risks as demand grew by 5-10X.
• High AI adoption costs, making automation challenging.
• Administrative inefficiencies, increasing operational overhead.

📄 Detailed Problem Statement

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🌟 Team Members

Hi! We are the "Data-ArchEvangelists Team" – playing with Data via AI.

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• Nitesh Sharma - Data Architect

• Mohit Tiwari - Lead Data Scientist

• Jyoti Singh - Lead Data Engineer

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🚀 Proposed Solution

To address these challenges, we implemented:

• AI-Powered Grading Services: Automating 80% of evaluations using NLP & Computer Vision.

  • a) Test 1 (Aptitude Test - Short Answer): 3 hours per candidate.
  • b) Test 2 (Architecture Submission): 8 hours per candidate.
  • c) Feedback Generation (Feedback Generation)AI enabled solution to provide feedback to tests.

• Real-Time Candidate Support: AI chatbots & tracking dashboards.

  • Candidate Experience

• Certification & Content Management: Efficient way to manage candidate as well as interviewer profile.

  • Content Management

• Event-Driven Microservices: Scalable cloud-based architecture.

  • Event Driven Processing

• Notification Email, SMS, Candidate updates and expiry alert.

  • Administration & Monitoring

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🚀 Challenges & Impact

🚧 Challenge	🎯 Impact
Manual Grading of Certification Tests	🔴 Delays grading turnaround, hard to scale without automation.
Feedback Generation Bottleneck	🟠 Inconsistent feedback, longer cycles reduce candidate throughput.
Outdated Certification Content	🔴 Difficult to update with industry trends, risking credibility.
Inefficient Administrative Processes	🟡 High overhead for managing candidate & expert data.
System Scalability Issues	🔴 Risk of system crashes, degraded performance, and slow response.
High Cost of AI Integration	🟠 Budget constraints could limit automation potential.
Poor Candidate Experience	🔴 Slow response times and outdated processes hurt reputation.

more information

🏗 System Architecture

Our system leverages microservices, AI-driven automation, and cloud-native deployment to deliver a scalable and cost-effective solution.

C1 System Context Diagram

• Modular Microservices: Independent grading, feedback, admin, and candidate services.
• Event-Driven Processing: Kafka, Redis Streams, and WebSockets for fast communication.
• Cloud-Native & Serverless AI Execution: AWS Lambda, Azure Functions.
• Scalability with Kubernetes (AKS/EKS) and auto-scaling policies.

🔹 Key Components in Context Diagram

1️⃣ External Entities

• Candidates – Register, submit tests, and track certification status.
• Admins – Manage test content, certification issuance, and AI grading reports.
• Hiring Companies – Validate candidates' credentials using Verification API.

2️⃣ Core System (Certifiable Inc. Certification System)

• Candidate Portal – Enables test submissions, certification tracking, and AI feedback visibility.
• Admin Dashboard – Monitors AI grading, updates test content, and ensures system performance.
• Hiring Company API – Allows third-party validation of certification details.

3️⃣ Internal Microservices

• AI Grading Service – NLP & CV models process test submissions for grading automation.
• Certification Service – Issues and verifies certifications & expiry dates.
• Event Processing Service – Kafka/Event Grid ensures asynchronous, scalable workflows.
• Notification Service – Sends real-time candidate updates (email, SMS, chatbot, alerts).
• Content Management System (CMS) – Allows admins to update test content dynamically.
• Admin & Monitoring Service – Provides real-time logging, AI monitoring, and system analytics.

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🎯 All Challenges Addressed?

🚧 Challenge	✅ Addressed By
Manual Grading Delays	AI Grading Service (Automates evaluations).
Inconsistent Feedback	AI Feedback Engine + AI Chatbot (Provides structured grading insights).
Outdated Certification Content	Content Management System (CMS) (Dynamically updates test materials).
Inefficient Admin Workflows	Admin Dashboard + Event Processing (Automates certification workflows).
System Scalability Risks	Event Processing + Microservices (Ensures load balancing & fault tolerance).
High Cost of AI	Serverless AI Execution + Cost Optimization via Hybrid Cloud (Efficient AI compute usage).
Poor Candidate Experience	Candidate Portal + Notifications + Chatbot (Enhances visibility & support).

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Architecture & Design

1. 001: AI-Powered Certification Evaluation System - Test1
2. 002: AI-Powered Certification Evaluation System - Test2
3. 003: AI-Driven Feedback Generation
4. 004: AI-Powered Content Generation
5. 005: AI-Powered Administrative Automation
6. 006: Scalable Microservices Architecture
7. 007: Cost-Optimized AI Deployment
8. 008: AI-Powered Candidate Experience

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🛠 Technical Implementation

Component	Technology Stack
Frontend	React.js, Next.js, Flutter
Backend API	FastAPI, Node.js
AI Grading & Feedback	OpenAI GPT-4, Hugging Face Transformers
Image & Diagram Processing	YOLO, Detectron2
Cloud Infrastructure	Kubernetes (AKS/EKS), AWS Lambda, Azure Functions
Messaging & Event Processing	Kafka, Redis Streams, RabbitMQ
Database & Storage	PostgreSQL, Azure CosmosDB, AWS DynamoDB
Monitoring & Logging	Prometheus, Grafana, ELK Stack

📄 Full Technical Breakdown

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📜 Architecture Decision Records (ADRs)

We documented key architectural decisions to ensure transparency and adaptability.

1. ADR-001: AI-Powered Certification Evaluation System - Test1
2. ADR-002: AI-Powered Certification Evaluation System - Test2
3. ADR-003: AI-Driven Feedback Generation
4. ADR-004: AI-Powered Content Generation
5. ADR-005: AI-Powered Administrative Automation
6. ADR-006: Scalable Microservices Architecture
7. ADR-007: Cost-Optimized AI Deployment
8. ADR-008: AI-Powered Candidate Experience

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🤝 How to Contribute

We welcome contributions! Follow these steps to contribute:

1. Fork the Repository
2. Clone the Repo: git clone https://github.com/your-repo/certifiable-ai.git
3. Create a Branch: git checkout -b feature-branch
4. Commit Your Changes: git commit -m 'Added new feature'
5. Push & Submit a PR: git push origin feature-branch

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📧 For inquiries, reach out at: [email protected]

🚀 Let’s redefine certification with AI!

_{Added by Data Arch Evanglist Team For Winter 2025 Kata: Architecture & AI on 17th March 2025}

_{Added by Data Arch Evanglist Team For Winter 2025 Kata: Architecture & AI on 17th March 2025}