🧾 InvoicePilot AI — Local-First Invoice Extraction with Open-Weight AI
What I Built
I built InvoicePilot AI, a local-first AI invoice extraction MVP designed to help freelancers and small businesses extract structured information from invoices.
Users can upload text-based PDF or TXT invoices, extract important fields using a locally running AI model, review and edit the results, and export the data as JSON or CSV.
✨ Features
- Extract vendor name, invoice number, dates, currency, subtotal, tax, and total.
- Process invoices using a locally running Ollama model.
- Support text-based PDF and TXT files.
- Review and edit extracted information.
- Export results to JSON and CSV.
- No hosted AI API is required for inference.
🛠️ How I Built It
- Python — application logic
- Streamlit — user interface
- Ollama — local model runtime
- Qwen 2.5 3B — invoice extraction
- PyMuPDF — PDF text extraction
🎥 Demo
I recorded a short screen demonstration showing invoice upload, AI extraction, field editing, and data export.
invoiceAi video.mp4 - Google Drive
drive.google.com
💻 Source Code
GitHub repository: https://github.com/Neeraja-Pyla/InvoicePilotAI
🌱 Why Does Open Innovation Matter?
Open-weight AI models make it easier for developers to experiment, learn, and build useful applications without depending on paid hosted AI APIs. Running inference locally can also give users greater control over their invoice data.
This project helped me explore practical AI integration and build a working application with tools I can run on my own computer.
🔍 Current Limitations
- Scanned PDF OCR is not implemented.
- Extracted information must be verified by the user.
- Invoice history is not saved between sessions.
🚀 What's Next?
I would like to explore OCR for scanned invoices, duplicate invoice detection, and batch processing.
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