🚀 Enterprise AI Architecture in C++

A modern, scalable, and production-ready Enterprise Artificial Intelligence Architecture built entirely in C++ for high-performance environments.

This project demonstrates how to design and implement an enterprise-grade AI system using clean architecture principles, modular design patterns, GPU acceleration, CI/CD readiness, and cloud deployment capabilities.

📌 Project Vision

To provide a robust C++ AI backbone suitable for:

🏦 Financial AI systems

🏥 Healthcare intelligence platforms

🏭 Industrial automation

🌍 Edge AI & IoT deployments

🧠 High-performance inference engines

Built with enterprise standards in mind.

🏗 Architecture Overview

The system follows a Clean Architecture + Modular Microservice Style:

enterprise-ai-cpp/ │ ├── core/ # AI core engine ├── models/ # ML model implementations ├── services/ # Business logic services ├── api/ # REST/gRPC interfaces ├── infrastructure/ # Logging, config, DB connectors ├── gpu/ # CUDA acceleration (optional) ├── tests/ # Unit & integration tests ├── docs/ # Documentation ├── CMakeLists.txt └── main.cpp

🔹 Architectural Principles

Clean separation of concerns

Dependency Injection

SOLID principles

Thread-safe components

GPU acceleration support (CUDA-ready)

CI/CD compatible

🧠 Core Features

✅ Modular AI engine

✅ Pluggable ML models

✅ GPU acceleration (CUDA support)

✅ REST/gRPC ready interface

✅ Structured logging

✅ Config-driven architecture

✅ Unit testing support

✅ Docker support

✅ CI/CD ready (GitHub Actions)

🛠 Tech Stack

• Language: C++20
• Build System: CMake
• GPU: NVIDIA CUDA (optional)
• Testing: GoogleTest
• API Layer: REST/gRPC ready
• Containerization: Docker
• CI/CD: GitHub Actions

⚙️ Build Instructions

1️⃣ Clone Repository git clone https://github.com/bundlab/enterprise-ai-cpp.git cd enterprise-ai-cpp

2️⃣ Create Build Directory mkdir build cd build

3️⃣ Configure Project cmake ..

If using CUDA:

cmake -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc ..

4️⃣ Build make -j$(nproc)

5️⃣ Run ./enterprise_ai

🧪 Running Tests

ctest

or

./tests/runTests

🐳 Docker Deployment

docker build -t enterprise-ai . docker run -p 8080:8080 enterprise-ai

🔥 GPU Acceleration (Optional)

This project supports NVIDIA CUDA acceleration.

Requirements:

• NVIDIA GPU
• CUDA Toolkit installed
• Proper CUDAToolkit_ROOT configured

Example:

• export CUDAToolkit_ROOT=/usr/local/cuda
• cmake ..

🔐 Enterprise-Grade Features

• Thread-safe AI pipeline
• Configurable inference engine
• Logging abstraction
• Structured error handling
• Horizontal scalability ready
• Container orchestration friendly

📊 Example Use Cases

• Industry Application
• Finance Fraud Detection
• Healthcare Medical AI Inference
• Retail Recommendation Engine
• IoT Edge Device Intelligence
• Manufacturing Predictive Maintenance

🧩 Extending the System

To add a new model:

• Create a new class in /models
• Implement the IModel interface
• Register inside ModelFactory
• Rebuild
• Clean and simple.

🔄 CI/CD Pipeline

• GitHub Actions workflow included for:
• Build validation
• Unit testing
• Static analysis
• Docker image build
• Push to main → automatic build triggered.

📈 Roadmap

ONNX Runtime Integration

TensorRT optimization

Distributed inference support

Kubernetes Helm chart

Model versioning system

🤝 Contributing

Contributions are welcome.

Fork the repo

Create a feature branch

Commit your changes

Push and create Pull Request

📜 License

MIT License

👨‍💻 Author

Abdullahi Bundi AI Systems Architect Enterprise AI & High-Performance Computing

⭐ If you find this project useful, please give it a star on GitHub!