# I built a support copilot for one-person shops that runs Gemma 4 on a laptop

> Source: <https://dev.to/devcarlat800/i-built-a-support-copilot-for-one-person-shops-that-runs-gemma-4-on-a-laptop-4bpc>
> Published: 2026-10-18 21:08:10+00:00

*This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle](https://dev.to/challenges/hacktoberfest-week2-2026-10-12)*

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.