PrivateLedger AI: Your Invoices, Your Device, Your Data 🔐

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

What I Built

Freelancers deal with invoices, receipts, payment records, and tax-related documents almost every day. Organizing them manually is tedious, and uploading them to an online AI service may expose sensitive client or financial information.

PrivateLedger AI is a privacy-first invoice and receipt organizer designed to run locally on your computer.

Drop in a batch of invoice images or PDFs, extract important information such as vendor names, dates, and amounts, review the results, organize the original files, and export verified records to CSV or Excel.

The idea is simple: make document management easier without requiring freelancers to send their financial documents to a cloud AI service.

Who would pay for it?

Freelancers, independent consultants, solo business owners, and small agencies that want to reduce administrative work while keeping their financial documents private.

Demo

GitHub repository: https://github.com/GunjanPatil-cloud/PrivateLedger-AI

Live demo: Not yet linked.

The application includes a demo mode using synthetic invoices, so its workflow can be explored without real financial documents.

Code

The project is open source under the MIT license.

Explore the source code, review the implementation, or run it locally:

Repository: https://github.com/GunjanPatil-cloud/PrivateLedger-AI

How I Built It

I built PrivateLedger AI around a local-first architecture:

Python + Streamlit: The application interface and workflow.

Tesseract OCR: Extracts text from invoice and receipt images.

PyMuPDF: Renders PDF pages for document processing.

Ollama + a local vision-language model: Helps extract structured information from documents on the user's machine.

Pandas: Handles the extracted records and exports.

SQLite: Optional local storage for extracted records.

The workflow combines OCR and local AI extraction, followed by a human review step. Users can correct the extracted information before exporting their records.

The project is designed so compatible local models can be changed through configuration rather than tying the entire application to a single proprietary AI API.

Why Does Open Innovation Matter?

For this project, privacy isn't just an extra feature. It is part of the reason the tool should exist.

Invoices can contain client names, addresses, payment details, tax identifiers, and other confidential information. A conventional cloud-based AI workflow may require sending that information to an external service.

By using open-source tools and a locally served model, PrivateLedger AI is designed to process documents on the user's own computer rather than upload their contents to a cloud AI provider.

Open innovation also gives this project three practical advantages:

1. Privacy and control

Users retain control over their documents and local processing environment. The application is designed not to send document data to a cloud AI API.

1. Freedom to change models

Ollama makes it possible to experiment with compatible models and choose one that fits the available hardware and extraction requirements.

1. A more sustainable side hustle

Local inference can avoid per-document cloud AI API charges. The trade-off is that users need compatible hardware, and model inference still consumes local computing resources.

The goal isn't to claim that open models are always more accurate or faster. It is to make a useful tool where control over data, flexibility, and reduced dependence on paid APIs are genuine advantages.

My Agent Session

Optional: Add a DevRelay agent session recording or link here if you have one.

My First User

The first milestone is to get PrivateLedger AI into the hands of a freelancer or independent consultant who regularly handles invoices.

I want to learn whether the tool saves them time, which fields need better extraction, and whether local processing is a meaningful reason for them to choose it over an online alternative.

I haven't included customer feedback or payment claims because these should come from a real user trial.

Prize Categories

Primary submission: Hacktoberfest Open-Source AI Challenge — Week 2: Side Hustle.

Potential additional category: Gemma, but only if I integrate and demonstrate Google's Gemma model in the project.

Thanks to the organizers and open-source community for encouraging small, practical AI projects that people can actually use.