ReceiptDesk: A Private AI Expense Inbox for Freelancers

ReceiptDesk: Private AI Receipt Sorting for Freelancers

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

What I Built

ReceiptDesk is a private, local-first receipt inbox for freelancers.

Freelancers do not usually have an accounting department. They have a folder of screenshots, a handful of emailed receipts, and a monthly reminder that taxes are coming.

ReceiptDesk turns receipt text into clean expense rows with a merchant, date, category, tax, and total.

The simple business is a lightweight subscription for solo freelancers who want an accountant-ready expense inbox without uploading every purchase to a third-party AI service. The product could charge for multiple workspaces, saved exports, and setup help while keeping the core local-first workflow available.

Demo

Live demo: ReceiptDesk

Try this flow:

1. Click Load demo receipts.
2. Filter the inbox by Software, Meals, or Travel.
3. Click Add receipt and paste receipt text.
4. Export the rows as a CSV file.
5. Try it with the network disabled: the local parser still works.

Code

Public repository: github.com/anshulchikhale30-p/wildquest

ReceiptDesk lives in the public/receiptdesk/ directory in the repository.

How I Built It

ReceiptDesk is a small static web app with a lightweight Node.js server. It stores the demo and user-added expense rows in browser local storage, so there is no account and no hosted database in the core flow.

The open/local AI path uses an Ollama-compatible endpoint when available. The app sends a bounded extraction prompt to an open-weight model such as Qwen2.5 3B Instruct or Llama 3.2 3B, asking for the merchant, date, category, tax, and total as JSON.

When no local model is reachable, ReceiptDesk uses a deterministic browser-side parser. It recognizes common total and tax labels and applies transparent category heuristics.

That fallback makes the demo inexpensive, private, and usable without internet access.

Why Does Open Innovation Matter?

Receipt data is sensitive. It can reveal where a freelancer works, who they meet, what tools they use, and how much they spend.

A closed hosted API would make the first prototype easy, but it would also turn every receipt into an upload and every extraction into a recurring inference cost.

The open/local approach makes a small side-hustle product more realistic:

• A freelancer can process receipts on a laptop with no internet.
• Receipts stay on the device unless the user deliberately connects a local model.
• The model can be swapped as better open-weight options become available.
• The extraction prompt and fallback behavior are inspectable and editable.
• There is no per-receipt API bill forcing the business into a high-volume pricing model.

Open AI is useful here not because it makes a more impressive demo, but because it changes the economics and privacy model of a tiny product someone could actually pay for.

My Agent Session

I did not include a DevRelay embed. The implementation is available in the public repository.

My First User

I have not landed a paying user yet.

The first validation target is a solo freelancer who currently collects receipts in a phone folder and sends them to an accountant once a quarter.

The first question I would test is whether local privacy and one-click CSV export are valuable enough to pay for every month.