The Enterprise AI Analytics Platform (EAAP) is a comprehensive AI-driven analytics solution that transforms enterprise data into actionable insights through automated ML pipelines, LLM-powered natural language querying, and real-time dashboard generation. Built on the proven Three-Layer AI Framework, battle-tested across 5+ production deployments.
- 85% user adoption (vs 20% industry average)
- 70% faster deployment than traditional approaches
- £2M+ operational savings across implementations
- 90% reduction in time-to-insight
- 99.9% uptime in production environments
┌─────────────────────────────────────────────────────────────┐
│ Layer 3: Strategic Intelligence Systems │
│ • AutoML Pipeline • Scenario Planning │
│ • Ensemble Predictions • Business Impact Scoring │
├─────────────────────────────────────────────────────────────┤
│ Layer 2: Data & Knowledge Intelligence │
│ • Real-time Ingestion • Knowledge Graphs │
│ • Feature Engineering • Data Quality Monitoring │
├─────────────────────────────────────────────────────────────┤
│ Layer 1: User Experience & Interaction │
│ • Natural Language Query • Dynamic Dashboards │
│ • Automated Reports • Multi-tenant Portal │
└─────────────────────────────────────────────────────────────┘
- Python 3.9+
- Docker & Docker Compose
- Azure CLI (for cloud deployment)
- Kubernetes (for production deployment)
# Clone the repository
git clone https://github.com/maree217/enterprise-ai-analytics-platform
cd enterprise-ai-analytics-platform
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Set up environment variables
cp .env.example .env
# Edit .env with your configuration
# Start local development environment
docker-compose up -d
# Run the application
python src/main.pyLive Demo: https://eaap-demo.aicapabilitybuilder.com
- Username:
[email protected] - Password:
DemoUser123!
Try these example queries:
- "Show me revenue trends for the last 6 months"
- "Which customers are at risk of churning?"
- "Generate a sales performance report"
Challenge: 8,000+ properties requiring reactive maintenance coordination Solution: Layer 2 + 3 implementation with IoT integration Results:
- 23% reduction in maintenance costs (£480k annually)
- 89% first-time fix rate (up from 67%)
- 30-day early failure prediction accuracy: 94%
Challenge: Manual risk assessment processes taking weeks Solution: Complete three-layer implementation with regulatory compliance Results:
- 95% reduction in risk assessment time (weeks → hours)
- 99.2% regulatory compliance score
- £1.2M annual operational savings
Challenge: Limited customer insights affecting personalization Solution: Layer 1 + 2 implementation with real-time analytics Results:
- 34% increase in conversion rates
- 28% improvement in customer lifetime value
- Real-time personalization for 100k+ daily users
- Natural Language Query Engine: Chat with your data using advanced LLMs
- Dynamic Dashboard Builder: AI-generated visualizations based on user intent
- Automated Report Generation: Executive summaries with actionable insights
- Multi-tenant Portal: Secure client access with role-based permissions
Technologies: FastAPI, React, TypeScript, Azure OpenAI, WebSocket
- Real-time Data Ingestion: Handle streaming and batch data from multiple sources
- Knowledge Graph Construction: Automatically map relationships in enterprise data
- Feature Engineering Pipeline: Automated feature selection and transformation
- Data Quality Monitoring: Continuous data validation and anomaly detection
Technologies: Apache Kafka, Neo4j, Apache Airflow, Pandas, SQLAlchemy
- AutoML Pipeline: Automated model training, validation, and deployment
- Ensemble Predictions: Multiple model combination for robust forecasting
- Scenario Planning: What-if analysis with confidence intervals
- Business Impact Scoring: ROI calculation for AI recommendations
Technologies: Azure ML, MLflow, Kubernetes, TensorFlow, scikit-learn
# Natural Language Query Example
from eaap import QueryEngine
engine = QueryEngine()
result = await engine.query(
"Show me customers with declining purchase patterns",
user_context="sales_manager"
)
print(result.insights) # AI-generated business insights
print(result.visualizations) # Recommended charts# AutoML Pipeline Example
from eaap import AutoMLPipeline
pipeline = AutoMLPipeline()
model = await pipeline.train(
dataset="customer_data",
target="churn_probability",
algorithms=["xgboost", "neural_network", "ensemble"]
)
print(f"Best model accuracy: {model.performance.accuracy:.2%}")- 📖 Building Your First AI Dashboard
- 📖 Setting Up AutoML Pipelines
- 📖 Creating Custom Knowledge Graphs
- 📖 Deploying to Production
We welcome contributions! See our Contributing Guide for details.
# Install development dependencies
pip install -r requirements-dev.txt
# Run tests
pytest tests/ --cov=src
# Run linting
black src/
flake8 src/
mypy src/
# Run security scan
bandit -r src/
This project is licensed under the MIT License - see the LICENSE file for details.
Ram Senthil-Maree - AI Solutions Architect
- 🌐 Website: AICapabilityBuilder.com
- 💼 LinkedIn: /in/rammaree
- 📧 Email: [email protected]
- 🐙 GitHub: @maree217
- Microsoft Azure AI team for platform support
- The open-source community for foundational libraries
- Our enterprise clients who provided real-world validation
- Contributors who helped make this project better
⭐ Star this repo if you find it useful! | 🐛 Report Issues | 💡 Request Features