This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.
A customer orders a 500g cake. The baker accepts it, buys ingredients, and starts preparing it.
An AI model can understand that message. The difficult question is what happens next.
A larger cake can affect ingredient quantities, preparation time, packaging, price, and other orders already promised. Quietly changing 500g to 1kg would erase the decision that matters.
That is why I built CrumbDesk: a local-first AI order-management tool for independent home bakers and small cottage bakeries.
CrumbDesk turns manually copied customer messages into structured, reviewable order details using Gemma 3 4B through local Ollama inference. It helps surface missing details, draft clarification replies, record change proposals, and preserve the baker’s authority over accepted orders.
Let the model interpret the message. Let deterministic code enforce the rules. Let the baker make the decision.
Home bakers often manage orders through WhatsApp, Instagram, SMS, and calls. A single message can mix flavour, quantity, weight, dietary preference, pickup time, payment statements, and special notes. Once more chats arrive, those details become difficult to find and easy to overlook.
CrumbDesk gives those details one dedicated workspace: orders needing review, confirmed orders, upcoming fulfilments, filters, local model status, and CSV export.
CrumbDesk’s dashboard gives the baker one local view of order status, schedules, and payment context.
The workflow starts with a deliberate boundary: the baker copies a customer message into CrumbDesk. The app does not automatically read WhatsApp or Instagram messages.
The baker pastes an informal customer message and requests structured extraction with local Gemma 3 4B.
Gemma helps identify the product, quantity, weight, date, time, dietary preference, fulfilment method, notes, and payment context. But its output is only a draft, never an accepted order.
For example, “Sunday” is not silently converted into a specific calendar date without enough information. “I paid via GPay” is recorded as a customer-reported claim, not proof that money cleared in a bank account. And one cake remains separate from one kilogram.
The review panel keeps extracted information editable and flags details that need baker clarification before confirmation.
If information is missing, CrumbDesk can generate an editable reply draft for the baker to copy into their chat app. This is intentionally deterministic: it uses known missing details instead of asking the model to make another unnecessary decision.
CrumbDesk prepares a polite, editable clarification reply without sending a message automatically.
The demo shows the complete flow: importing a customer message, reviewing the extracted order, confirming it, recording a later change request, and preserving the result in the order history.