Byte-Sized n8n AI Series: The Sovereign AI Stack
Break free from cloud dependency and take full control of your intelligence stack. In this intensive 90-minute session, you will learn how to architect a “Sovereign AI” engine from the ground up.
- Overview
- Audience
- Prerequisites
- Curriculum
Description:
Break free from cloud dependency and take full control of your intelligence stack. In this intensive 90-minute session, you will learn how to architect a "Sovereign AI" engine from the ground up. We will deploy n8n—the powerful workflow automation tool—using Docker, and bridge it to Ollama to run world-class models like DeepSeek and Llama 3 locally. By the end of this session, you’ll have a live, self-hosted automation hub that processes data without it ever leaving your local network.
Duration:
90 minutes
Course Code: BDT 533
Learning Objectives:
After this course, you will be able to:
- Deploy and manage a containerized n8n instance with persistent storage
- Configure local LLM inference using Ollama
- Implement a hybrid "Cloud-Fallback" strategy using “openrouter.ai”
- Establish a secure connection between an automation engine and a local "Brain."
By the end of this AI Bootcamp, you will have the confidence and competence to tackle building Agentic AI systems.
Developers, DevOps enthusiasts, and AI hobbyists looking to move away from proprietary SaaS models. Ideal for those who value data privacy and want to build automation workflows that run entirely on their own hardware.
Basic familiarity with the Command Line (Terminal/PowerShell), use Docker or npm as environment to install n8n
Course Outline:
- Infrastructure: Deploying the Foundation
- Docker Advantage: Why containerization is key for sovereign stacks
- Docker Compose Walkthrough: Configuring docker-compose.yml for n8n
- Persistence & Environment: Setting up volumes for data retention & securing the instance with environment variables
- Lab: Launching the n8n container and initial setup
- The “Brain” Connection: Local vs Cloud Inference
- Ollama Deep-Dive: Installing Ollama and pulling models (DeepSeek-R1 / Llama 3)
- Local Hardware Optimization: Understanding CPU vs GPU utilization for local LLMs
- The Safety Net: Configuring Open Router (openrouter.ai) for high-reasoning tasks for cloud fallback
- Lab: Testing the Ollama API locally via terminal




