I Built a Local AI to Turn a Shop's Evening Spreadsheet Hunt Into a Fix Queue

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

Imagine a small-shop owner at closing time. The shutters are down, the counter is clean, and tomorrow's work is already waiting in four spreadsheets. Sales say what moved. Inventory says what remains. Purchase orders say what should have arrived. Invoices say what someone expects to be paid.

This owner is a fictional protagonist, but the task is specific: find the few rows that deserve attention before they become tomorrow's problem. A spreadsheet can be accurate and still demand too much of the person reading it. The work is making connections across files, remembering the assumptions, and deciding what to do next.

I built QuietOps to make that review smaller. It turns operational spreadsheets into an issue inbox, with a calculation and a route back to the source for every flag. Local AI explains those supplied facts and proposes a next step. The owner reviews a draft, approves it for export, and carries out any follow-up themselves.

There are three checks in the working application: stock cover shorter than supplier lead time, overdue purchase orders with quantity still outstanding, and exact or fuzzy duplicate invoice candidates. An invoice candidate is a request to verify records. It is not a fraud accusation or a claim that money was paid twice.

The interface has eight working views: overview, issue inbox, fix queue, inventory, suppliers, import and mapping, quality review, and audit history. That sounds like a lot of screens for a quieter evening, so the main path is deliberately short: load or import, inspect a flag, read its explanation, review a draft, export.

The visual tone matters to that workflow. A shop owner should be able to distinguish a priority from a weak estimate without feeling surrounded by alarms. Stock, delivery, and invoice candidates have separate labels; assumptions remain attached to the issue; the model's availability is visible.

The narrated walkthrough is a real 1920×1080 browser recording, about two minutes long. It uses a running backend and genuine local inference. Its narration was synthesized locally with Kokoro-82M using the user-selected British voice George, bm_george. It includes burned-in English captions and a separate SRT file.

Every shop record in the demonstration is synthetic. The fixture has 60 products, 12 suppliers, 90 days of sales, 24 purchase-order lines, and 38 invoice rows. The recording begins with an empty workspace and selects that fictional shop. It then follows an oat-milk flag through its source evidence, real model explanation, purchase-review draft, approval, export, and audit trail.

The originals remain downloadable throughout. A separate browser test exercises manual CSV upload, explicit mapping confirmation, an invalid row, and a proposed correction. The video uses demo selection so the main review story stays readable.

The public walkthrough on YouTube and public source repository are available. The five screenshots in this article are hosted in that repository at a fixed commit; every image URL was checked against the original file.

A little less on your plate. Turn a shop’s inventory, sales, deliveries and invoice spreadsheets into a short, evidence-backed review queue.

QuietOps is a working local application: Next.js + TypeScript + Tailwind + shadcn/Radix components, FastAPI, Pandas, DuckDB, SQLite, and Ministral 3 8B through Ollama. Deterministic code finds candidates; local AI explains the supplied facts and drafts a review step. A person edits and approves drafts before export. It does not send messages, place orders, or alter financial records.