ReceiptPilot Local: Your Privacy-First AI Receipt & Invoice Organizer

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

What I Built

ReceiptPilot Local is a privacy-first, AI-powered receipt and invoice organizer designed for freelancers, small business owners, students, and side hustlers.

Managing receipts and invoices manually can be time-consuming. Important details such as invoice numbers, dates, vendor names, taxes, and totals are often scattered across PDFs, images, and documents.

ReceiptPilot Local simplifies this process by using AI to extract important information from uploaded receipts and invoices and organize it into an editable table.

✨ Key Features

• 📄 Multiple File Formats: Upload PDF, TXT, Markdown, CSV, and image files.
• 🤖 AI-Powered Extraction: Extract invoice details into structured JSON using a locally running AI model.
• 🔒 Privacy First: Process documents locally with Ollama instead of relying on a cloud-based AI API.
• ✏️ Editable Results: Review and correct extracted information before exporting it.
• 📊 Data Export: Export organized invoice data as CSV or JSON.
• 🧾 Sample Receipt: Test the application using a sample receipt.

Who Would Pay for It?

Freelancers, consultants, small business owners, independent contractors, and side hustlers who want to simplify expense tracking without sending sensitive financial documents to third-party AI services.

The project could evolve into a subscription-free desktop application, a paid business tool, or a self-hosted solution for teams that prioritize data privacy.

Demo

Project: ReceiptPilot Local — AI-Powered Receipt & Invoice Organizer

The application runs locally on your computer using Streamlit and Ollama.

• Sample Receipt: Upload a sample invoice to test the extraction workflow.
• Live Demo: A publicly deployed demo is not available yet; the current version is designed to run locally.

Code

GitHub Repository: https://github.com/001abhimanyu/ReceiptPilot-Local.git

The project includes:

• app.py — Streamlit application and AI-powered document processing.
• requirements.txt — Python dependencies.
• README.md — Setup instructions and project documentation.
• .gitignore — Files that should not be committed to GitHub.

The application is designed to be simple to run, easy to understand, and open for community contributions.

How I Built It

I built ReceiptPilot Local using Python, Streamlit, and open-source AI tools.

Technology Stack

• Python: Core application logic and document processing.
• Streamlit: Interactive web interface.
• Ollama: Runs the AI model locally.
• Llama 3.2: The default locally configured model for structured information extraction.
• PyPDF: Extracts text from PDF documents.
• Tesseract OCR: Extracts text from supported receipt and invoice images.
• Pandas: Organizes extracted information and supports data export.

How It Works

1. The user uploads a receipt or invoice.
2. The application extracts text from the document or uses OCR for images.
3. The extracted text is sent to the locally running AI model through Ollama.
4. The model identifies relevant fields, such as vendor name, invoice number, date, subtotal, tax, and total.
5. The application displays the extracted information in an editable table.
6. The user reviews the results and exports them as CSV or JSON.

The goal was to build something practical that combines local AI inference with a simple interface to solve a real-world problem.

Note: OCR and document extraction accuracy can vary depending on image quality and document format. Users should review extracted financial information before relying on it.

Why Does Open Innovation Matter?

Open innovation made it possible to build an AI-powered application without making a proprietary cloud AI service a mandatory dependency.

By using open-source tools and a locally running model, ReceiptPilot Local explores a different approach to handling sensitive financial documents.

Benefits of Open Innovation

• Privacy and Control: Users can process documents locally without needing to upload them to a hosted AI provider.
• Accessibility: Developers and learners can experiment with AI without building their applications around a paid proprietary API.
• Customization: Users can experiment with different locally supported models and improve extraction prompts.
• Transparency: Developers can inspect the application code, understand the workflow, and contribute improvements.
• Community Collaboration: Other developers can add features such as expense categorization, duplicate detection, monthly reports, and improved OCR.

Open innovation is not just about making software free. It is about giving people the freedom to learn, adapt, improve, and build on existing technology.

ReceiptPilot Local is my attempt to demonstrate how open-source AI can make everyday financial workflows simpler while giving users greater control over their data.

My Agent Session

I built the initial application by working through the project structure, document-processing workflow, local AI integration, and export functionality.

Agent session: Add your DevRelay session link here if you recorded one. Otherwise, you can remove this optional section.

Prize Categories

Select the applicable partner categories from the official challenge page before submitting. I have not listed any categories here because eligibility should be confirmed against the challenge's official rules.

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What's Next?

I would love to improve ReceiptPilot Local with community contributions, including:

• Automatic expense categorization.
• Monthly and yearly expense summaries.
• Duplicate receipt detection.
• Multi-currency support.
• Better OCR for low-quality receipt images.
• Export to accounting-friendly formats.
• Batch processing for multiple invoices.

If you're interested in open-source AI, Python, privacy-first applications, or developer tools, contributions and feedback are welcome!

Built for the Hacktoberfest Open-Source AI Challenge — Week 2: Side Hustle.