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World desk5 min

Alisha Catat: How a Local Gemma Assistant Turns Print Orders Into Records

Alisha Catat is an early local-AI workflow for a small print shop: Gemma extracts order details, but code handles pricing and the owner reviews each record before saving.

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Alisha Catat turns typed, informal print orders into reviewable records for a small shop in Cisontrol, Rancah, Indonesia. Gemma extracts order details; the app’s configured price list and deterministic code handle matching and arithmetic. The owner checks or edits each confirmation before it is saved, so the model does not approve transactions on its own.

What Alisha Catat does

The shop handles work such as banners, photo prints, ID cards, and invitations. Before the app, prices were kept on a poster and orders and transactions were written down by hand. Alisha Catat accepts an order in ordinary language, extracts fields such as the customer name, service, dimensions, quantity, and unit, then matches the request against the shop’s price list and calculates the amount.

For example, the developer gives the Indonesian order “Pak Dedi pesan spanduk 3×1 dua lembar, DP 100rb.” Its approximate English meaning is: Pak Dedi orders two 3 × 1 metre banners, with a 100,000 IDR down payment. This is an example of phrasing the app is intended to handle, not evidence about the frequency of any particular customer query.

The app presents the extracted details in a confirmation card. Once the owner confirms or corrects them, the record is stored in SQLite. Confirmed records can be used to create PDF, Excel, or image receipts and daily, weekly, and monthly sales reports. These details are described by developer Joni Ilman Pahmi in his October 4, 2026 project account.

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How the model and transaction logic are separated

Gemma’s role is to interpret the order and extract fields. Application code—not the language model—matches items to the shop’s configured prices, parses down payments, and performs decimal arithmetic. That distinction matters: a plausible-looking model response is not treated as an authoritative price or payment calculation.

The owner’s review is another important control. A mistaken name, dimension, quantity, or unit could affect a real order and its price, so the confirmation step lets the person responsible catch and correct extraction errors before they become saved transactions. A manual form is available if inference is too slow or unavailable.

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What was tested, and what the results can show

Pahmi reports testing three configurations with five invented sample orders. The examples were not representative shop data, and five cases cannot establish general accuracy or a dependable speed estimate.

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Configuration Author-reported extraction result Author-reported time per order
Gemma 3 4B, longer prompt 5 of 5 Approximately 37 seconds
Gemma 3 4B, shorter prompt 5 of 5 Approximately 23 seconds
Gemma 3 1B, shorter prompt 4 of 5; the app flagged the miss with a warning Approximately 11 seconds

These are results reported by the developer for the five invented samples, not an independent benchmark. Although the shorter-prompt run took less time in this test, the account does not isolate prompt processing as the cause or show that the timing difference is repeatable. A larger, more varied sample, repeated timing runs, and real orders used with permission would be needed to support broader conclusions.

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The reported runs used gemma3:4b on an AMD A8-9600 CPU with 8 GB of RAM and no dedicated GPU; the account does not give separate hardware details for each row. Actual performance can vary with hardware, model, prompt, and the order itself. The reported figures are useful context for this particular experiment, not a promise that another computer or shop will see the same results.

How local and offline the workflow is

The project uses Ollama and Gemma 3 for local inference. According to Pahmi, typed order text is processed by the locally running model, and the core typed-order workflow does not require an internet connection. That can reduce the need to send typed customer and order details to a remote model provider.

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Offline inference does not establish that the entire application is offline or secure. The author notes that browser speech recognition may depend on a remote service, depending on the browser and implementation, and does not show that audio is processed locally. Microphone access generally requires a secure context such as HTTPS or localhost; a phone opening the app through a plain HTTP address on a local network may not have microphone access. Device-level keyboard dictation may be an alternative, depending on the device.

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The shop owner’s computer has no microphone, and the current workflow is mainly typed orders. The owner’s positive comment about offline use should therefore not be read as evidence that voice ordering is part of regular shop operations.

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What the shop owner has said

Pahmi says the owner, Asep, tried the app and gave permission for his feedback to be quoted. Asep wrote: “intinamah mantap, aplikasina ringan, mudah di akses bisa offline oge. jeng mempermudah rekap harian sampai bulanan toko saya”. An approximate English translation is: “Overall, it’s great. The application is lightweight, easy to access, works offline too, and makes it easier to recap my shop’s sales from daily to monthly.” This is useful early feedback, but the account does not establish long-term adoption or quantify time saved.

Pahmi describes the goal this way: “The goal isn’t to replace the shop owner with an AI agent. It’s to reduce repetitive administrative work while keeping the owner in control of every transaction.” The design choices—human confirmation, deterministic price calculations, and a manual-entry route—reflect that division of responsibility.

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What the implementation uses

The project account lists Ollama and Gemma 3 for inference; SQLite with Drizzle ORM for storage; Solid-Vue JS and Tailwind CSS for the interface; Tabler Icons and Chartist; and pdf-lib, ExcelJS, and Decimal.js for documents, spreadsheets, and decimal arithmetic.

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Google describes Gemma 3 as an open-weight model family with text and image input, multilingual support, and deployment options that include laptops and desktops in its Gemma 3 model card. Its Ollama guide says Gemma variants can run on laptops or small computing devices without a GPU; it also notes that quantized GGUF variants reduce compute requirements and may reduce output quality. These are general platform statements, not independent validation of Alisha Catat’s accuracy or performance.

Operational and distribution considerations

Local inference is only one part of protecting shop records. The project account identifies backups, access controls, error handling, and data recovery as remaining operational needs. Keeping data on a local computer does not, by itself, protect the app, database, backups, or any connected services from loss or unauthorized access.

Google’s Gemma Terms of Use, last modified April 1, 2026, include obligations concerning use restrictions, notices, and distribution of Gemma or model derivatives. The project account does not specify whether or how model weights are redistributed or document a legal review, so it does not establish the project’s compliance status. Anyone deciding whether to distribute an application or model weights should review the current terms directly.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

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