A shop that has been selling for a while usually has a review export nobody has read to the end. The answer to "what should we fix first?" is in there, but finding it means reading every line and keeping a tally in your head. A language model can do the reading if you ask it a narrow question: for each review, is it a complaint, praise or a question? The steps below do that in the Datasets screen of the osFoundry desktop app, with a model running on the same computer. The screenshots use an example export of 312 reviews from a small pottery shop, made up for this walkthrough.

Upload the export

Most shop back ends can export reviews as a CSV file. One review per line, with a header row, is all this needs; the example has three columns, date , product and review .

In the app, open Models, then Datasets, and click Upload in the top right. Pick the CSV file. Each line becomes a row of a new dataset named after the file, and a message confirms the row count: 312 rows in the example.…