The application reads the full spreadsheet or text file and proposes how to treat each field before touching the contents. The person who knows the file reviews that proposal, with a chance to catch an abbreviated heading or a column the software has misunderstood.

Identifiers get particular care. A string of digits may be the name of a part, with no business being added up. A blank may mean nobody has supplied the information yet, and a tool shouldn't fill it with a guess. The reviewer doesn't have to inspect every cell to get started, but they do settle what the important fields mean and what must stay as it is.

A locally running model suggests improvements, and the application checks each suggestion before accepting it. The grid updates as the work moves through the file, with before-and-after examples available to inspect.

Rejected suggestions are recorded next to accepted changes, so the reviewer can see where cleanup stopped short. The application and the model both run locally, so the business file isn't sent to another AI service during the process.

The download contains the cleaned data, a change record and a quality report, in familiar spreadsheet formats. Whoever receives it can see what changed, what still needs review and why a particular value was left alone.