This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
I built Local Ledger AI, a local-first receipt and invoice parser designed specifically for freelancers, micro-agency owners and sole traders.
Small business owners routinely process sensitive financial documents containing client names, tax identification numbers, residential addresses and confidential project scopes. Passing all that data through third-party cloud APIs poses serious privacy risks, and monthly subscription costs for traditional scanning software quickly stack up.
Local Ledger AI runs structured optical character recognition and field extraction entirely on the user’s local machine. It automatically extracts key details such as vendor names, dates, invoice numbers, tax amounts, line items and expense categories. Users can parse hundreds of receipts in seconds, review the structured data instantly, and export clean summaries directly into CSV or JSON formats for their accounting software. Freelancers and privacy-conscious sole traders would gladly pay a one-off fee for software that handles their bookkeeping without risking client confidentiality or tying them into monthly API bills.
Code
The code repository is available on GitHub at https://github.com/rbstrachan/hacktoberfest.
How I Built It
The site uses local open-weight vision models such as Llama 3.2 3B Vision and Phi-3 Mini running directly through Ollama and WebGPU.
When a user drops an invoice or receipt image into the interface, the app streams the file directly to the local model endpoint via a REST interface or directly in the browser runtime. The system uses structured JSON schemas within the prompt pipeline to guarantee that the open-weight model outputs predictable key-value pairs representing the document structure.
The frontend is constructed using plain HTML5, CSS3, and JavaScript, ensuring lightweight execution, instant loading times, and full offline functionality without any backend web server infrastructure.
Why Does Open Innovation Matter?
Client data privacy is absolute. Proprietary API providers retain the right to log, inspect or train on incoming payloads unless users pay exorbitant enterprise rates. By using an open-weight model running locally, financial data never touches a server or leaves the device, providing total compliance with strict data protection standards like GDPR out of the box.
The economics of closed APIs break the business model for small software developers. Charging a freelancer a modest flat fee for software becomes impossible if every scan incurs a micro-transaction bill from a closed API provider. Local inference reduces per-parse API costs to absolute zero, allowing the tool to operate as a low-cost, perpetual software product without running up unsustainable infrastructure overheads.
Open models allow seamless model swapping. If a user needs faster local inference on a low-spec laptop, they can toggle to a quantised lightweight model; if they require complex multi-currency detection, they can load a specialised fine-tuned open weights model instantly.
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