Building an AI-Powered Workflow Orchestration Stack

Many companies want to automate internal workflows with AI. Support tickets, bug reports, documentation search, customer questions, and developer tasks can all be partially automated.

In this guide we will build a simple but powerful AI workflow orchestration system using open-source tools:

• Open WebUI — internal AI interface for employees
• n8n — workflow orchestrator
• Qdrant — semantic search database for AI knowledge
• PostgreSQL — structured data storage

This stack lets you build things like:

• AI-assisted support ticket triage
• Internal company AI assistant
• Automated bug reporting workflows
• AI answering questions using company documentation

Everything here can run on one Linux server.

1. What We Are Building

Imagine a SaaS company workflow:

1. A customer submits a support request.
2. The system automatically:
   • analyzes the request with AI
   • searches company documentation
   • creates internal tasks
   • suggests a reply to support staff

The workflow looks like this:

This is called workflow orchestration.

2. Server Requirements

For a small company setup:

Recommended server:

• Ubuntu 22.04
• 16 GB RAM
• 4 CPU cores
• 100 GB disk

Install Docker first.

sudo apt update
sudo apt install docker.io docker-compose -y

Enable Docker:

sudo systemctl enable docker
sudo systemctl start docker

3. Project Structure

Create a working directory:

mkdir ai-workflows
cd ai-workflows

Create the main configuration file:

nano docker-compose.yml

4. Docker Compose Setup

Paste the following configuration.

version: "3"

services:

  postgres:
    image: postgres:15
    environment:
      POSTGRES_USER: ai
      POSTGRES_PASSWORD: ai123
      POSTGRES_DB: workflows
    volumes:
      - postgres_data:/var/lib/postgresql/data
    ports:
      - "5432:5432"

  qdrant:
    image: qdrant/qdrant
    ports:
      - "6333:6333"
    volumes:
      - qdrant_data:/qdrant/storage

  n8n:
    image: n8nio/n8n
    ports:
      - "5678:5678"
    environment:
      - DB_TYPE=postgresdb
      - DB_POSTGRESDB_HOST=postgres
      - DB_POSTGRESDB_DATABASE=workflows
      - DB_POSTGRESDB_USER=ai
      - DB_POSTGRESDB_PASSWORD=ai123
    depends_on:
      - postgres

  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    ports:
      - "3000:8080"
    volumes:
      - openwebui_data:/app/backend/data

volumes:
  postgres_data:
  qdrant_data:
  openwebui_data:

Start everything:

docker compose up -d

Docker will download and start all services.

5. Access the Tools

After startup, open your browser.

Open WebUI: http://SERVER-IP:3000

Create your admin account.

n8n dashboard: http://SERVER-IP:5678

Qdrant API: http://SERVER-IP:6333

PostgreSQL: localhost:5432

At this point your AI infrastructure is running.

6. Add an AI Model

Open WebUI needs a model backend. The easiest option is to run Ollama on the host.

Install Ollama (local model server):

curl -fsSL https://ollama.com/install.sh | sh

Run a model:

ollama run llama3

Now connect Open WebUI to: http://host.docker.internal:11434

Your AI assistant is ready.

7. Storing Knowledge in Qdrant

AI becomes much more useful when it can search company knowledge.

Examples of documents to store:

• Product documentation
• Support procedures
• API documentation
• Internal manuals

Example collection creation:

curl -X PUT http://localhost:6333/collections/company_docs \
-H 'Content-Type: application/json' \
-d '{
  "vectors": {
    "size": 384,
    "distance": "Cosine"
  }
}'

Documents are converted into embeddings and stored in Qdrant. AI can now search them semantically.

8. Creating the Workflow in n8n

Open the n8n dashboard. Create a new workflow.

Example automation: Webhook → AI Analysis → Knowledge Search → Store Ticket → Suggest Reply

Step 1: Webhook trigger
POST /support-ticket

Step 2: AI analysis
Send ticket text to the AI model.

Step 3: Knowledge search
Query Qdrant for similar documentation.

Step 4: Save ticket
Insert structured data into PostgreSQL:

INSERT INTO tickets
(customer_email, summary, category, priority)
VALUES ($1,$2,$3,$4)

Step 5: Suggested reply
The AI combines ticket content and documentation results to generate a draft response.

9. Example AI Prompt

A practical prompt used inside the workflow:

You are a SaaS support assistant.

Customer message:
{{ticket_text}}

Relevant documentation:
{{qdrant_results}}

Write a clear support reply.
Do not invent information.

This produces a high-quality suggested response for the support team.

10. Real Company Use Cases

Once the system works, you can expand it.

AI documentation assistant

Employees ask questions about internal systems.

Automated support classification

Tickets automatically routed to the correct team.

Developer bug report generation

AI extracts logs and creates developer tickets.

Customer onboarding assistant

AI answers new customer questions using documentation.

11. Why This Stack Works

This setup separates responsibilities:

• Open WebUI — Human interface to AI
• n8n — Automation and orchestration
• Qdrant — AI knowledge memory
• PostgreSQL — Structured business data

Together they form a modern AI workflow platform.

12. Final Advice

Start simple. Do not try to automate everything at once.

A good first project is: "AI suggests replies to support tickets."

Once that works, expand to:

• Ticket routing
• Knowledge search
• Developer workflows

This is exactly how many companies introduce AI into real business operations. Small automation steps, built on a solid foundation.

Technical Glossary