I Built a Smart Invoice Assistant Using Local AI

The idea

Invoices contain important information such as client names, invoice numbers, amounts, and due dates. Extracting these details manually can take time, especially when handling multiple invoices.

For Hacktoberfest DEV Challenge Week 2, I built Smart Invoice Assistant, a small web application that uses a locally running AI model to extract invoice details and generate payment reminder drafts.

What does it do?

The application currently has two main features:

• Invoice analysis: Paste invoice text and let AI extract the client name, invoice number, amount, currency, and due date.
• Payment reminder generator: Generate a polite payment reminder using the invoice text and copy it for later use.

Tech stack

I built this project using:

• HTML for the structure
• CSS for the user interface
• JavaScript for frontend interactions
• Node.js for the local server
• Ollama to run the AI model locally
• Qwen 2.5 3B as the open-weight AI model

Why local AI?

I wanted to experiment with running an AI model on my own machine instead of depending on a paid external AI API.

The application sends invoice text to the Ollama service running locally on my computer. This also gave me an opportunity to learn how a frontend communicates with a backend and how a backend can call a local AI model.

Local processing can help reduce reliance on external AI services, although users should still protect sensitive invoice information and secure their local environment.

What I learned

While building this project, I practised connecting HTML, CSS, and JavaScript with a Node.js server, handling API requests, displaying AI responses, and using Git and GitHub to manage and publish my code.

I also learned that a useful AI application does not need to be complicated to start with. A focused workflow can be a good foundation for future improvements.

Try the project

GitHub repository: https://github.com/Abhishek-gitty/smart-invoice-assistant

The current version runs locally on a computer with Node.js, Ollama, and the required model installed. It is not a hosted web demo yet.

Current limitations and future plans

This is an early version, and there is plenty of room to improve it.

Some features I would like to explore next are:

• PDF invoice upload and text extraction
• Saving invoice history
• Better input validation and error handling
• More structured invoice summaries
• Improved accessibility and responsive design

Final thoughts

This project was a hands-on way for me to explore local AI and connect it to a practical web application.

I would love to hear suggestions on what feature I should build next and how I can make the application more useful for people who manage invoices.

Thanks for reading!