Local AI for Polish technical descriptions

The editor needs fewer corrections to check

Good help lets you prepare a readable text without establishing the facts about the product again. The editor can focus on whether the sentences are clear to the reader and fit the rest of the catalogue. That is why it is worth showing the proposal next to the approved note. It is then easier to compare the equipment and the part names before the description is published.

Company terms matter too. If the catalogue calls a part a “mounting plate” (“płyta montażowa”), swapping that name for a loose synonym can make it harder to find in other material. Inflection by grammatical case is a natural part of a Polish sentence; changing the name of a part is something the editor has to watch. The model's assessment should cover the vocabulary the team actually uses.

What to check in your own Polish descriptions

A person who knows the products and an editor can review a chosen group of texts together. The first checks whether the description kept the information from the approved material. The second judges whether the Polish sentences really need less work. Both assessments are needed: a faithful rewrite of a clumsy note may not help, and an elegant description with changed equipment has to be rejected.

Compare the time it takes to reach a finished description with your current way of working, including reading the proposal and the corrections. Run the trial on the hardware you plan to use, and count the wait for the proposal too. If the model keeps the terminology in a short note, the next step can be a description with several items of equipment. The team will then see whether the distinctions stay clear in material closer to daily work.

Sometimes a description template with fields for included and optional items is enough. That is a sensible solution when most product sheets share the same layout. A model is worth comparing when approved notes differ in form and often need rewording. We describe a wider way of comparing tools in the article on buying local AI with a trial on your own documents.

Where the text is processed and how good the Polish is are separate decisions

A company may want to prepare descriptions on its own computer, for example before a new catalogue is published. The local model then runs its computations on the chosen device. Whether it handles the product's Polish terminology well has to be judged on the team's descriptions. Where it runs does not answer that question.

The LM Studio documentation distinguishes local, cloud and remote models. For the chosen local mode, the vendor describes offline work with the model and documents once the model and the runtime are prepared. Downloading, searching for models and updates use the network. A proposed solution therefore has to name the specific mode of work, and you cannot infer that it is local from the app's name alone.

A description from note to catalogue

Syntalith can prepare an app for editing Polish technical descriptions, fitted to the company's local environment and catalogue work. We suggest starting by comparing a template with the model on a chosen group of products, plus a view where the editor reads the approved note next to the proposed description. Together we agree where the app takes the material from and where the text goes after checking.

The person responsible for products confirms the naming and the scope of equipment, and the editor judges the corrections still to be made. A trial like this lets you decide whether the model makes the work easier on the planned hardware and whether it is worth connecting the tool to the catalogue. We agree the scope of the build and of later support from the team's needs; pricing information is on the pricing page.

Tell us about a correction that keeps coming back in the descriptions, and how a text moves from note to catalogue today. From that we will propose a scope of work that fits your editing process and the required place of processing.

What a local model for technical descriptions costs and when it pays back

Where to start

In the free process review we choose one task and 20 to 100 documents the team has already worked on, and within 2 working days you get a written summary with a price. We roll out the local model as part of private LLMs, and the app around it as an AI app.