What I Built
I built InvoiceMate AI, a Python-based invoice management application designed to help freelancers, small businesses, and independent professionals save time managing invoices.
Small businesses often spend valuable time manually reading invoices, entering invoice numbers, checking totals, and organizing financial records. InvoiceMate AI helps simplify this process by extracting invoice information and organizing it in one place.
Key Features
- 📄 Upload invoice PDFs.
- 🔍 Extract invoice text using PDF processing and OCR tools.
- 🤖 Use local AI to identify important invoice details.
- ✏️ Review and edit extracted information before saving.
- 🗃️ Store invoice records in a local SQLite database.
- 📊 View invoice information and totals on a dashboard.
- 📤 Export invoice data to CSV.
Who would pay for it?
Freelancers, consultants, bookkeepers, and small businesses that want a straightforward way to organize invoices and reduce manual data entry could benefit from a tool like this. A future version could offer paid features such as bulk processing, advanced reporting, and integrations with accounting software.
Demo
Code
🔗 GitHub Repository: https://github.com/arya179/invoicemate-ai
The project is built with Python and Streamlit, with local database storage and AI-assisted invoice extraction.
How I Built It
I built InvoiceMate AI using open-source tools and a locally running AI model.
Technology stack:
- Python — application logic
- Streamlit — interactive user interface and dashboard
- Ollama + Gemma 2B — local AI-powered invoice information extraction
- PyMuPDF — extracting text from PDF invoices
- Tesseract OCR — supporting text extraction from scanned documents
- SQLite — storing invoice records locally
- Pandas — handling invoice data and CSV exports
The workflow is simple: upload an invoice, extract its text, use the local AI model to identify relevant fields, review the results, and save the information.
I chose a local model because I wanted to learn how open-weight AI can be integrated into a practical application without relying entirely on a paid hosted AI API.
Why Does Open Innovation Matter?
Open innovation made it possible to experiment with AI without building a model from scratch or depending entirely on a closed, paid API.
Using open-source libraries and the Gemma model through Ollama gave me the flexibility to run inference locally, customize the application, and learn how the different components work together.
Local inference can also reduce the need to send invoice contents to an external AI service. This can be valuable when handling financial documents, although privacy still depends on the application's configuration, storage, and deployment.
Most importantly, open-source tools made this project accessible for learning and experimentation, with the possibility for other developers to inspect, improve, and extend it.
My Agent Session
I used Antigravity IDE to help with the development workflow and project organization. I also explored DevRelay as part of my Hacktoberfest workflow.
Optional: Add your saved DevRelay agent session here if you have one available.
Prize Categories
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Building InvoiceMate AI helped me explore how local AI can solve a real business problem. I hope to keep improving it with better extraction accuracy, duplicate detection, and support for more invoice formats.
Thanks for checking out my project! 🚀
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