I Built a Local AI Order Manager for Small Businesses

Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 💸 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.

Hacktoberfest 开源 AI 挑战赛第二周：副业项目提交 💸 这是我为 Hacktoberfest 开源 AI 挑战赛第二周提交的副业项目。我构建了 MakerFlow，这是一个专为小型企业设计的轻量级订单管理工具。小型企业通常通过 WhatsApp、Instagram、电话和线下到店接收订单，导致客户详情、尺寸、颜色、材质、价格和截止日期等信息很容易散落在各种对话和笔记中。

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.

店主可以：将客户消息粘贴到应用程序中；使用 Gemma 将消息转换为结构化的订单草稿；在保存前审查并更正草稿；追踪客户、联系方式、商品、数量、材质、颜色、尺寸及其他规格；记录价格、定金和余额；添加截止日期以及自提或配送详情；通过生产工作流管理订单：新建 → 已报价 → 已付款 → 生产中 → 已就绪 → 已发货 → 已完成；高亮显示逾期订单；按活跃、逾期或全部筛选订单；将订单队列导出为 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.

目标客户是那些需要简单订单管理台，但又不想支付昂贵电商或生产管理平台费用的独立创作者或小企业主。客户可以通过小额月度订阅、一次性设置服务或定制化版本来使用 MakerFlow。

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.

演示：主要工作流为：将客户消息粘贴到 MakerFlow 中；点击“使用 Gemma 起草”；审查提取的订单详情；保存订单；追踪生产状态；通过仪表板监控余额和配送信息。示例消息：“你好，我是 Maya。我需要三个 18 厘米宽的黑色手机支架，价格 45 美元。我周五来取。我的电话是 555-0100。” MakerFlow 会将该消息转换为可编辑的订单草稿，无需店主手动重新输入每一项细节。

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

代码：源代码可在此处获取：GitHub 仓库 Anyaxx/makerflow。该项目包含：FastAPI 后端、SQLite 持久化存储、基于浏览器的仪表板、Gemma/Ollama 集成、Docker 配置、Docker Compose 部署设置、CSV 导出、订单状态工作流以及定金与余额追踪。

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.

构建过程：MakerFlow 使用了 Python、FastAPI、SQLite、HTML、CSS、JavaScript、Docker、Ollama 和 Gemma 3 4B。AI 功能的设计是有意聚焦的。Gemma 不会做出生产决策，也不会自动与客户沟通。它仅创建一个草稿，供店主在保存前进行审查。应用程序通过 Ollama 将粘贴的客户消息发送给 Gemma，并请求结构化的 JSON 数据。

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.

后端随后会对价格、货币和日期进行标准化处理，将初始状态设置为“新建”，并将草稿返回给浏览器。店主可以在保存前编辑草稿。FastAPI 会验证订单（包括价格和定金数值），并将最终订单存储在 SQLite 中。

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.

部署架构为：浏览器 → FastAPI + MakerFlow 仪表板 → 私有 Docker 网络 → Ollama → Gemma 3 4B。该应用程序通过 Docker Compose 打包，部署在 DigitalOcean Droplet 上。应用程序和 Ollama 在独立的容器中运行，而模型和订单数据库则使用持久化的 Docker 卷。

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.

GitHub Copilot 帮助我塑造了产品构思、设计了订单工作流、实现了 FastAPI 后端和浏览器 UI、通过 Ollama 集成了 Gemma、测试了 API 行为，并准备了 Docker 部署。

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.

为什么开放创新很重要？开放创新使该项目对小型企业具有实用价值。托管的 AI API 虽然可以提取订单详情，但会将客户消息发送到外部服务，并可能对每次请求收取费用。通过 Gemma 和 Ollama，MakerFlow 可以在企业自行控制的基础设施上运行模型。

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.

这提供了：更可预测的运营成本、对客户数据更强的控制力、无需支付按条计费的 AI API 费用、能够在本地或私有 DigitalOcean 服务器上运行模型的能力、未来更换模型或提示词的自由，以及一个聚焦的 AI 工作流，而非昂贵的通用聊天机器人。

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.

对于小型企业而言，重点不在于拥有最大的模型，而在于拥有一个实用、私密且负担得起的工具来消除重复性工作。MakerFlow 使用开源 AI 完成一项特定任务：将非正式的客户请求转换为结构化的订单草稿。店主始终掌控着最终订单。

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.

我的 Agent 会话：该项目是使用 GitHub Copilot 构建的，会话内容涵盖了产品设计、实现、Gemma 集成、本地测试、Docker 设置和仓库创建。

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: Copy a real customer message. Paste it into MakerFlow. Let Gemma create a draft. Review the details. Save and track the order.

我的第一位用户：MakerFlow 是为一位经营小型 3D 打印业务的朋友设计的。产品方向源于小型打印店实际接收工作的方式：非正式的消息、电话和对话，而非结构化的电商订单。第一位用户的工作流被设计得非常简单：复制真实的客户消息，粘贴到 MakerFlow 中，让 Gemma 创建草稿，审查详情，保存并追踪订单。

Prize Categories Best use of GitHub Copilot Best use of Gemma

奖项类别：GitHub Copilot 最佳使用奖、Gemma 最佳使用奖。