I Built a Local AI Order Manager for Small Businesses

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

Small businesses often receive orders through WhatsApp, Instagram, phone calls, and walk-ins. Customer details, sizes, colors, materials, prices, and deadlines can easily become scattered across conversations and notes.

I built MakerFlow, a lightweight order-management tool for small businesses.

A shop owner can:

Paste a customer message into the app.
Use Gemma to turn the message into a structured order draft.
Review and correct the draft before saving it.
Track the customer, contact information, item, quantity, material, color, size, and other specifications.
Record the price, deposit, and remaining balance.
Add a due date and pickup or delivery details.
Move the order through a production workflow: New → Quoted → Paid → In production → Ready → Shipped → Completed
Highlight overdue orders.
Filter orders by active, overdue, or all.
Export the order queue as CSV.

The target customer is a solo maker or small business owner who needs a simple order desk without paying for a large e-commerce or production-management platform.

Customers could pay for MakerFlow through a small monthly subscription, a one-time setup service, or a customized version for their shop.

Demo

The main workflow is:

Paste a customer message into MakerFlow.
Click Draft with Gemma.
Review the extracted order details.
Save the order.
Track its production status.
Monitor the balance and delivery information from the dashboard.

Example message:

Hi, I’m Maya. I need three black phone stands, 18 cm wide, for $45. I can pick them up Friday. My number is 555-0100.

MakerFlow turns that message into an editable order draft instead of requiring the owner to retype every detail manually.

Code

The source code is available here:

GitHub repository: Anyaxx/makerflow

The project includes:

FastAPI backend
SQLite persistence
Browser-based dashboard
Gemma/Ollama integration
Docker configuration
Docker Compose deployment setup
CSV export
Order status workflow
Deposit and balance tracking

How I Built It

MakerFlow uses Python, FastAPI, SQLite, HTML, CSS, JavaScript, Docker, Ollama, and Gemma 3 4B.

The AI feature is intentionally focused. Gemma does not make production decisions or automatically communicate with customers. It creates a draft that the shop owner reviews before saving.

The application sends a pasted customer message to Gemma through Ollama and requests structured JSON:

{
"customer_name": "Maya",
"contact": "555-0100",
"item": "phone stands",
"specifications": "3 units, black, 18 cm wide",
"price": 45,
"currency": "USD",
"due_date": "",
"delivery_details": "Pickup Friday",
"notes": ""
}
The backend then normalizes the price, currency, and date, sets the initial status to new, and returns the draft to the browser.

The owner can edit the draft before saving it. FastAPI validates the order, including price and deposit values, and stores the final order in SQLite.

The deployment architecture is:

Browser
↓
FastAPI + MakerFlow dashboard
↓
Private Docker network
↓
Ollama
↓
Gemma 3 4B

The application is packaged for deployment on a DigitalOcean Droplet using Docker Compose. The app and Ollama run in separate containers, while the model and order database use persistent Docker volumes.

GitHub Copilot helped me shape the product idea, design the order workflow, implement the FastAPI backend and browser UI, integrate Gemma through Ollama, test the API behavior, and prepare the Docker deployment.

Why Does Open Innovation Matter?

Open innovation made this project practical for a small business.

A hosted AI API could extract order details, but it would send customer messages to an external service and potentially introduce a usage charge for every request. With Gemma and Ollama, MakerFlow can run the model on infrastructure controlled by the business.

That provides:

More predictable operating costs
Greater control over customer data
No per-message AI API charge
The ability to run the model locally or on a private DigitalOcean server
The freedom to change models or prompts later
A focused AI workflow instead of an expensive general-purpose chatbot

For a small business, the important point is not having the largest model. The important point is having a useful, private, affordable tool that removes repetitive work.

MakerFlow uses open-source AI for a specific job: converting informal customer requests into structured order drafts. The owner remains in control of the final order.

My Agent Session

The project was built with GitHub Copilot, and the session included the product design, implementation, Gemma integration, local testing, Docker setup, and repository creation.

My First User

MakerFlow was designed for a friend who runs a small 3D-printing business.

The product direction came from how small print shops actually receive work: informal messages, phone calls, and conversations rather than structured e-commerce orders.

The first user workflow is intentionally simple: