SuccessionAI: AI-powered Employee IDP Platform

SuccessionAI is a comprehensive talent management platform that leverages artificial intelligence to identify, develop, and prepare high-potential employees for future leadership roles. The platform uses advanced analytics, gap analysis, and personalized development planning to optimize succession planning strategies.

Architecture

This is a Turborepo monorepo containing:

SuccessionAI/
├── apps/
│   ├── ai-backend/          # Python FastAPI backend with AI services
│   ├── web/                 # Next.js frontend application
│   └── server/              # Additional server services
├── packages/
│   ├── eslint-config/       # Shared ESLint configurations
│   ├── typescript-config/   # Shared TypeScript configurations
│   └── ui/                  # Shared React component library
└── turbo.json               # Turborepo configuration

Key Features

Nine-Box Matrix Analysis

• Employee Segmentation: Categorizes employees based on performance and potential
• Visual Analytics: Interactive charts and data visualization
• Batch Processing: Analyze multiple employees simultaneously

AI-Powered Gap Analysis

• Skills Gap Detection: Identifies missing competencies for target roles
• Performance Analysis: Compares current vs required ratings
• LLM Integration: Uses Llama-3.3-70B for intelligent recommendations

Readiness Prediction

• ML-based Assessment: Predicts employee readiness for role transitions
• Multi-factor Analysis: Considers performance, potential, skills, and experience
• Confidence Scoring: Provides prediction confidence levels

Individual Development Plans (IDP)

• Automated Generation: Creates personalized development plans
• Mentor Matching: Intelligent pairing with senior employees
• Resource Recommendations: Curated learning materials and courses
• Milestone Tracking: Time-based development goals

Technology Stack

Backend (ai-backend)

• Framework: FastAPI with Python 3.8+
• AI/ML: Groq (Llama-3.3-70B), LangChain, scikit-learn
• Database: MongoDB with PyMongo
• APIs: RESTful endpoints with automatic OpenAPI documentation

Frontend (web)

• Framework: Next.js 15+ with React 19+
• Language: TypeScript
• Styling: CSS Modules
• Development: Turbopack for fast development

Infrastructure

• Monorepo: Turborepo for efficient build system
• Package Management: npm/yarn workspaces
• Code Quality: ESLint, TypeScript, Prettier

Installation & Setup

Prerequisites

• Node.js 18+ and npm/yarn
• Python 3.8+
• MongoDB database
• API Keys: Groq API key for LLM features

1. Clone Repository

git clone https://github.com/ankitku3101/SuccessionAI.git
cd SuccessionAI

2. Install Dependencies

# Install all workspace dependencies
npm install

# Install Python backend dependencies
cd apps/ai-backend

# Windows
python -m venv myenv
myenv\Scripts\activate

# Linux/Mac
python3 -m venv venv
source venv/bin/activate

pip install -r requirements.txt

3. Environment Configuration

Create .env files in respective applications:

apps/ai-backend/.env

MONGO_URI=your_mongodb_connection_string
GROQ_API_KEY=your_groq_api_key
TAVILY_API_KEY=your_tavily_api_key  # Optional for web search
API_BASE_URL=http://localhost:8000

4. Database Setup

Ensure MongoDB is running and accessible. The application expects these collections:

• employees - Employee profiles and data
• success_roles - Role definitions and requirements
• gap_analysis - Gap analysis results
• idp - Individual Development Plans

Development

Start All Services

# From root directory
npm run dev

Individual Services

# Backend only
cd apps/ai-backend
python main.py

# Frontend only  
cd apps/web
npm run dev

# Specific workspace
npm run dev --filter=web

Access Applications

• Frontend: http://localhost:3000
• Express Backend API: http://localhost:5000
• FastAPI Backend API: http://localhost:8000
• API Documentation: http://localhost:8000/docs

AI Components

Gap Analysis Agent

• Purpose: Identifies skill and performance gaps
• Model: Llama-3.3-70B via Groq
• Fallback: Rule-based analysis when LLM unavailable

Readiness Prediction Model

• Type: Machine Learning classifier
• Features: Performance, potential, skills, experience
• Output: Ready/Developing/Not Ready with confidence scores

IDP Generator

• Workflow: Data retrieval → Gap analysis → Recommendations → Resource matching → Mentor pairing
• Integration: MongoDB + LLM + Web search (Tavily)

Turborepo Configuration

The turbo.json defines build and development pipelines for efficient monorepo management.

Testing & Quality

Backend Testing

cd apps/ai-backend
python -m pytest

Code Quality

# Lint all workspaces
npm run lint

# Type checking
npm run check-types

Deployment

Production Build

# Build all apps
npm run build

# Build specific app
npm run build --filter=web

Environment Variables (Production)

Ensure all required environment variables are set in your production environment.

Contributing

1. Fork the repository
2. Create 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

License

This project is private and proprietary.

Support

For support and questions, please contact the development team.

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