This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Picture a one-person online shop selling handmade ceramics. The owner makes the mugs, photographs them, packs them... and every evening answers the same emails: "my mug arrived cracked", "my parcel is late", "can I get a refund?". She already knows the answers. She just has to write them again, consistently, every single time.
Little Kiln Support Copilot drafts those replies for her. You paste a customer message; it finds how you solved similar cases before and writes a draft that follows your own policies. You review it and send it.
Who would pay: solo makers and small shops on Etsy, Shopify or their own site, for whom support eats hours but a full helpdesk subscription is overkill. Because everything runs on open models on the owner's laptop, there is no per-message AI bill, so a flat few euros a month (or a one-time setup fee) would be a sustainable price.
Three real runs from the command line, for a sample shop called "Little Kiln Ceramics".
A broken vase. The customer asks for their money back; the copilot recognizes a damaged item and applies the shop's damaged-item policy: free replacement within 2 days, no need to return the broken piece, a photo of the damage.
A late parcel. It promises to check the tracking and offers the 10% discount code (SORRY10) the shop used in a similar case. Notice the "In the past, we have offered..." wording: a small model sometimes leaks internal phrasing, which is exactly why a human reviews every draft.
A question nobody asked before. No past case is similar enough, so instead of inventing an answer, the draft says the owner will check.
Draft replies to customer messages for one-person online shops, based on how you solved similar cases before. The AI runs on open models on your own laptop, so customer messages are never sent to a third-party AI API and every draft costs nothing to generate.
Built for the [Hacktoberfest 2026 Open-Source AI Challenge, Week 2: Side Hustle](https://dev.to/challenges/hacktoberfest-week2-2026-10-12).
If you sell handmade products online on your own, a big part of your evenings goes into answering the same questions again and again: a parcel arrived broken, a delivery is late, someone wants a refund. You already know the answers. You just have to write them again, every time, in a consistent way.
The sample shop is Little Kiln Ceramics, a fictional one-person shop selling handmade ceramics.
The copilot is a small retrieval-augmented generation (RAG) pipeline in Python, built around two open-weight Google models running locally with Ollama:
<=>) and keeps only those above a threshold. e2b-it-qat build, about 4 GB, runs on a laptop with no GPU) receives the message plus the retrieved cases, with instructions to use only those policies and never invent prices or deadlines.
Three things I learned while testing:
This is my first hackathon. I work a lot with AI tools, so I paired with an AI assistant to move fast on the parts that were new to me: pgvector and local models.
For a side hustle, the economics decide everything, and open models change them: The trade-off is honest: a bigger closed model might write smoother prose. But for drafts that a human reviews anyway, a free, private, local model is the better deal.