Relying entirely on third-party cloud AI APIs can lead to skyrocketing monthly costs and high risks of sensitive data leaks. I specialize in designing and deploying 100% self-hosted, private AI Agents using advanced open-source LLMs. By combining these local intelligence models with n8n—the ultimate node-based workflow engine—I build autonomous ecosystems that handle customer support, execute complex data transformations, and sync enterprise tools behind your secure firewall.

1. Private LLM Infrastructure & Autonomous Agents

I bridge the gap between heavy enterprise operations and local AI models to ensure complete data compliance and zero token-fee liabilities:

• Ollama & Local Model Tuning: Serving state-of-the-art open models (like Llama 3, Mistral, or Qwen) locally on your infrastructure, utilizing advanced quantization techniques to preserve precision while minimizing VRAM footprint.
• RAG (Retrieval-Augmented Generation): Connecting local vector databases (like Qdrant, Milvus, or pgvector) to your internal document repositories (PDFs, Wikis, CRMs). This lets your private AI agent instantly answer company-specific queries with pinpoint accuracy and no hallucinations.

2. Hyper-Automated n8n Integration Flows

Instead of basic point-to-point scripts, I assemble deep event-driven architectures that sync software platforms via self-hosted n8n instances:

• Multi-App Synchronizations: Binding dynamic tools together—such as routing Webhooks from your application to instantly trigger updates across Discord/Slack, internal databases, CRM pipelines, and email marketing desks.
• AI-Powered Logic Gates: Injecting local LLM sub-nodes directly inside n8n workflows to read incoming customer support emails, automatically determine customer sentiment, extract key entities, and draft responses autonomously.

Docker Compose For Local AI Agent & n8n Ecosystem

# Self-Hosted Stack: Merging Local LLM Processing Engine with n8n Automation Node
version: '3.8'

services:
  ollama:
    image: ollama/ollama:latest
    container_name: local_ai_engine
    volumes:
      - ./ollama_data:/root/.ollama
    ports:
      - "11434:11434"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu] # Leverages local hardware acceleration

  n8n:
    image: docker.n8n.io/n8nio/n8n:latest
    container_name: workflow_orchestrator
    ports:
      - "5678:5678"
    environment:
      - N8N_HOST=automation.local.lan
      - OLLAMA_HOST=http://local_ai_engine:11434
    volumes:
      - ./n8n_data:/home/node/.n8n
    restart: always