Schaeffler Mobility Insight Platform

An AI-powered web application for exploring, validating, and implementing emerging mobility technology trends. Built for Schaeffler's Advanced Innovations in Mobility Solutions, this platform leverages large language models to generate strategic insights and actionable implementation roadmaps.

Project Overview

The Mobility Insight Platform transforms how Schaeffler identifies and evaluates disruptive technologies in the mobility sector. By combining AI-generated insights with structured assessment frameworks, the platform accelerates innovation decision-making from months to minutes.

Key Benefits

• Rapid Technology Scouting: Generate and evaluate multiple technology trends in real-time
• Structured Assessment: Apply Schaeffler's P³ innovation framework consistently
• Data-Driven Decisions: Track confidence scores, variance metrics, and implementation readiness
• Strategic Alignment: Ensure technologies align with Schaeffler's core competencies and market position

Features

Core Workflow

• 4-Phase Innovation Pipeline:
  1. Identification: Define mobility use-case, sector, and specific demands
  2. Scouting: AI generates 3 disruptive technology trends with confidence scores
  3. Validation: Comprehensive assessment
  4. Implementation: Market-ready solutions with partnership strategies and roadmaps

Intelligent Components

• Multi-Model LLM Support: OpenAI GPT-4 Turbo or Google Vertex AI (Gemini)
• Fallback Mechanisms: Ensures consistent output even during API failures
• Variance Analysis Dashboard: Track output consistency across multiple trials
• Quality Assessment Framework: Automated testing for diversity, compliance, and performance metrics
• Session Persistence: Large data handling through file-based storage

Analytics & Monitoring

• Real-time Metrics: API calls, token usage, latency tracking
• Historical Analysis: Complete audit trail of all queries and responses
• Regression Testing: Automated baseline comparison for output quality
• Export Capabilities: CSV/JSON data export for further analysis

🛠 Tech Stack

Backend

• Python 3.8+ - Core application logic
• Flask 2.0+ - Web framework
• SQLAlchemy - Database ORM with connection pooling
• PyMySQL - MySQL database connector

Frontend

• Bootstrap 5.3 - Responsive UI framework
• Jinja2 - Template engine
• Markdown - Content rendering with table support

AI/ML

• OpenAI API - GPT-4 Turbo integration
• Google Vertex AI - Gemini model support
• tiktoken - Token counting and optimization

Analytics

• Streamlit - Interactive dashboards
• Plotly/Pandas - Data visualization and analysis
• NumPy/SciPy - Statistical computations

Infrastructure

• MySQL 5.7+ - Primary database
• Redis (optional) - Session caching
• Docker (optional) - Containerization

Installation & Setup

Prerequisites

• Python 3.8 or higher
• MySQL 5.7 or higher
• OpenAI API key or Google Cloud project with Vertex AI enabled

Step 1: Clone Repository

git clone [https://github.com/Marawan-Y/Mobility-Insight-Bot]
cd mobility-insight-bot

Step 2: Create Virtual Environment

python -m venv venv

# Windows
venv\Scripts\activate

# Linux/Mac
source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Configure Environment

Create a .env file in the root directory:

# Flask Configuration
SECRET_KEY=your-secret-key-here
FLASK_ENV=development

# Database Configuration
DB_HOST=localhost
DB_PORT=3306
DB_USER=your_db_user
DB_PASSWORD=your_db_password
DB_NAME=mobility_bot

# LLM Configuration (choose one)
LLM_PROVIDER=openai  # or "vertex"

# OpenAI Configuration
OPENAI_API_KEY=your-openai-api-key

# OR Google Vertex AI Configuration
VERTEX_PROJECT=your-gcp-project-id
VERTEX_LOCATION=us-central1
VERTEX_MODEL=gemini-1.0-pro

# Optional: Feedback Form
FEEDBACK_FORM_URL=https://your-google-form-url

Step 5: Initialize Database

# Create database
mysql -u root -p
CREATE DATABASE mobility_bot CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;
EXIT;

# Run database setup
python fix_database.py
python setup_assessment_db.py

Step 6: Verify Installation

python health_check.py

Usage

Running the Application

Option 1: Quick Start (Windows)

setup.bat

Option 2: Manual Start

python Final_Structured_app.py

The application will be available at http://localhost:5000

Running Analytics Dashboard

streamlit run assessment_dashboard.py

Dashboard will open at http://localhost:8501

Workflow Example

1. Navigate to http://localhost:5000
2. Select your mobility use-case (e.g., "People mover mobility")
3. Choose sector (e.g., "RoboTaxi")
4. Describe your demand (e.g., "Optimize navigation for urban environments")
5. Review AI-generated technology trends
6. Validate selected trends for detailed assessment
7. Implement chosen technology with market-ready solutions

Advanced Features

Generate Variance Trials

python generate_variance_trials.py --use-case "People mover mobility" --sector "RoboTaxi" --demand "navigation" --trials 5

Run Quality Assessment

python assessment_runner.py --use-case "Your use case" --sector "Your sector" --demand "Your demand" --trials 3

Batch Processing

python batch_assessment.py --test-cases assessments/test_cases.json --trials 5

Database Management

Backup & Restore

# Create backup
python bin/backup_automation.py

# Switch between local/server databases
python bin/config_manager.py

Remote Access Setup

python bin/setup_remote_access.py

Testing & Quality

Run Tests

# Regression testing
python regression_testing.py

# API connectivity test
python test_openai_apikey.py

Performance Monitoring

• Access metrics at /metrics endpoint
• View quality dashboard via Streamlit
• Check logs/ directory for detailed logs

Project Structure

mobility-insight-platform/
├── Final_Structured_app.py      # Main Flask application
├── assessment_dashboard.py      # Streamlit analytics dashboard
├── llm_quality_assessment.py    # Quality assessment framework
├── templates/                   # HTML templates
│   ├── index.html              # Main interface
│   └── base.html               # Base template
├── static/                      # CSS, JS, images
├── bin/                         # Database & deployment tools
├── assessments/                 # Test cases & reports
└── logs/                        # Application logs

Contributing

We welcome contributions! Please follow these steps:

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

Development Guidelines

• Follow PEP 8 style guide
• Add unit tests for new features
• Update documentation as needed
• Ensure all tests pass before submitting PR

License

This project is proprietary software owned by Schaeffler AG. All rights reserved.

Support

For issues or questions:

• Check the documentation

Acknowledgments

• Schaeffler Advanced Innovations Team
• OpenAI for GPT-4 API
• Google Cloud for Vertex AI
• Open source community contributors

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📚 Cite this software

@software{Younis_MobilityInsightBot_2025,
  author    = {Marawan Younis},
  title     = {Mobility Insight Bot (v1.0.0)},
  year      = {2025},
  publisher = {Zenodo},
  version   = {1.0.0},
  doi       = {10.5281/zenodo.<17247162>},
  url       = {(https://doi.org/10.5281/zenodo.17247162)}
}