文件禅

FileNeatAI, released under the Chinese name 文件禅, is a desktop utility built around a familiar frustration: the folders we accumulate over months of work and study rarely stay organized on their own. Rather than asking users to open files one by one and decIDE where each belongs, the tool hands that judgment to an AI model and produces an organized ouTPUt folder in a single pass.

What it does

The core workflow is simple. A user dRAGs a target folder into the APPlication window and presses the organize button. The software reads the contents of the files, infers what each one is about, and groups files that share a topic or type into matching subfolders. When the progress bar finishes, a new output folder appears containing the sorted structure, so the original directory is left untouched and can be reviewed before anything is moved permanently.

Alongside sorting, FileNeatAI offers a batch renaming mode. Files with irregular or meaningless names can be dropped in, and the model proposes new names that are eASIer to identify later. If a suggestion does not fit, a retry control generates an alternative before the user confirms and applies the rename.

Key capabilities

• Content-aware classification that sorts files by what is inside them rather than by extension alone.
• Support for image files in addition to common document formats, according to the developer's description.
• Batch renaming with AI-suggested names and a retry option for alternate proposals.
• Choice between a hosted online model and a local model configured in the settings panel.
• Local model mode reportedly rEMOves the file-count limit imposed on online processing and keeps file contents on the user's machine.

Who it is for

The tool targets students, office workers, administrative staff, and anyone who regularly handles large volumes of downloaded or received files. It is most useful for people who postpone cleanup because manual sorting is tedious, and for users with privacy concerns who prefer not to send document contents to a remote service.

How to get started

Install the application on a Windows machine and drag in a folder you want to test on a copy of your data first. For local Operation, install an OLlama CLIent, pull a compatible model such as a Chinese-tuned Llama 3 variant, then enter the local server address, an API key placeholder, and the model name in the settings. Online mode requires no such setup.

Pricing and access caveat

No reliable public pricing Information was available at the time of writing, and the developer has not documented a formal tier structure in the provided material. Treat any cost, veRSIon, or licensing detail as subject to change and verify directly with the vendor before relying on it for regular workflows.