CoreSight

AI-Driven Enterprise Delivery & Workforce Intelligence System

Hackathon Project: DataZen - Somaiya Vidyavihar University

CoreSight transforms raw engineering activity into actionable business intelligence. It leverages AI to analyze commits, match developers to tasks, detect burnout risks, and provide comprehensive workforce analytics.

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Features

AI-Powered Intelligence

• Smart Task Matching - Automatically matches developers to tasks based on skills and work history using vector embeddings
• Skill Extraction - Extracts skills from commit diffs and task descriptions using LLM analysis
• Duplicate Detection - AI-powered issue duplicate checking
• Profile Evolution - Tracks and updates developer skills based on their actual work
• Job Requisition Generation - Automatically generates job postings when no matching internal candidates are found

Analytics & Insights

• Maker vs. Mender Score - Categorizes developer work (new features vs. refactoring vs. cleanup)
• True Task Costing - Calculates actual task costs by aggregating work sessions
• Burnout Detection - Analyzes context switching patterns to detect burnout risk
• Project Budget Tracking - Tracks time and cost against project budgets

Integrations

• GitHub Webhooks - Real-time commit and PR tracking
• Jira Integration - Task synchronization and status tracking

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Architecture

CoreSight/
├── backend/                    # FastAPI Backend (Python)
│   ├── main.py                 # Application entry point
│   ├── routes/                 # API endpoints (10 routers)
│   ├── services/               # Business logic layer
│   ├── ai/                     # AI/ML utilities
│   ├── entities/               # Pydantic models
│   ├── scripts/                # DB init & seed scripts
│   └── utils/                  # Database & auth utilities
└── frontend/                   # React Frontend (TypeScript)
    └── src/
        ├── pages/              # Application pages
        ├── components/         # Layout & route guards
        ├── contexts/           # Auth context
        └── lib/                # API client & utilities

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Backend

Tech Stack

• Framework: FastAPI
• Database: MongoDB (Motor async driver)
• AI/ML: Google Generative AI (Gemini), OpenAI
• Auth: JWT (python-jose)

API Routes

Route	Description
/auth	Authentication (login, logout)
/users	User management & profiles
/tasks	Task CRUD & assignment
/projects	Project management
/jobs	Job requisition management
/webhooks	GitHub/Jira webhook handlers
/issues	Issue tracking with duplicate detection
/commits	Commit analysis & skill extraction
/analytics	Analytics & reporting endpoints
/careers	Public careers page API

AI Modules

Module	Purpose
embeddings.py	Generate text embeddings & calculate similarity
matching.py	Find best matching users for tasks
skills.py	Extract skills from tasks & code
validation.py	LLM-based candidate evaluation
reports.py	Generate no-match reports & job descriptions
analysis.py	Commit analysis & duplicate detection

Data Models

• User - Developer profiles with skills, embeddings, and hourly rate
• Project - Projects with budget tracking
• Task - Work items with multi-user assignment
• Sprint - Sprint tracking with goals and dates
• Issue - Issues with duplicate detection
• Commit - Git commits with skill extraction
• JobRequisition - AI-generated job postings

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Frontend

Tech Stack

• Framework: React 19
• Build Tool: Vite 7
• Language: TypeScript
• Styling: TailwindCSS 4
• Charts: Recharts
• Routing: React Router 7

Pages

Page	Route	Description
Dashboard	/dashboard	Overview with key metrics
Users	/users	User management list
User Form	/users/new, /users/:id	Create/edit users
User Analytics	/users/:userId/analytics	Individual user analytics
Tasks	/tasks	Task management
Projects	/projects	Project management
Analytics	/analytics	Team-wide analytics
Activity	/activity	Activity feed
Job Requisitions	/jobs	Manage job postings
Careers	/careers	Public job listings (no auth)
Login	/login	Authentication

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🛠️ Setup

Prerequisites

• Python 3.10+
• Node.js 18+
• MongoDB

Backend Setup

cd backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your API keys and MongoDB URL

# Run server
python main.py

Frontend Setup

cd frontend

# Install dependencies
npm install

# Run dev server
npm run dev

Note: The frontend uses a Vite dev-server proxy (/api → http://localhost:8000) so no VITE_API_URL configuration is needed during local development.

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Environment Variables

Backend (.env)

Variable	Description
MONGODB_URL	MongoDB connection string
MONGODB_DB_NAME	Database name (default: coresight)
GOOGLE_API_KEY	Google Generative AI API key
OPENAI_API_KEY	OpenAI API key (optional)
JWT_SECRET_KEY	Secret for JWT token signing
API_PORT	API server port (default: 8000)

Frontend (.env)

Variable	Description
VITE_API_URL	Backend API URL (leave empty for local dev — Vite proxy handles it)

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API Documentation

Once running, access the interactive API docs:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

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Authentication

CoreSight uses JWT-based authentication with two user roles:

Role	Authentication	Description
Admin	Local password	Full access, requires password
Employee	External SSO (future)	Limited access, no local password

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Key Analytics Features

Maker vs. Mender Score

Analyzes commit patterns to categorize work:

• New Feature - Mostly additions (building new capabilities)
• Refactoring - Mostly modifications (improving existing code)
• Cleanup - Mostly deletions (removing technical debt)

Burnout Detection

Monitors context switching to identify at-risk developers:

• Tracks daily task switches
• Flags users with >4 switches/day as "High Risk"

True Task Costing

Calculates actual costs by:

• Aggregating all work sessions
• Multiplying by user hourly rates
• Comparing against project budgets

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Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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License

This project was created for the DataZen Hackathon at Somaiya Vidyavihar University.

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👥 Team

CoreSight Intelligence Engine — Version 1.1.0