FileSeek vs Precision File Search

FileSeek is a Windows file-search app that can look through local paths, network shares, and synced cloud folders such as Dropbox and OneDrive. Queries can be simple, use regular expressions, or use advanced text syntax. It uses multithreading and does not depend on background indexing. Advanced IFilters allow searches to process non-text files such as Excel and PDF. Pro adds saved search profiles that can sync between computers, multiple search tabs, and CSV or HTML result exports. The free plan is limited to personal use and lacks those Pro features. Pro Standard costs $19, paid once for a lifetime license for one computer; other lifetime licenses cover personal computers, a site, or company-owned sites. The current release supports Windows 10 version 1607 or higher, Windows 11, and Windows Server 2016, 2019, and 2022. Downloads include an installer, ZIP build, and MSI installer, and FileSeek can also be installed using WinGet. SHA1 hashes are published to help check downloads. Support is included with free and Pro licenses.

Precision File Search is a free desktop application for finding, classifying, and understanding local files on Windows, macOS, and Linux. It combines classic search with semantic search using a retrieval-augmented generation pipeline, and can answer natural-language questions with summarized responses. A trainable machine-learning model sorts scattered documents into categories users define. The maker says the application and AI models can operate locally, without cloud dependency, and that files, queries, and personal data are not sent to external servers. The project lists support for 18 languages, including English, Arabic, Chinese, French, German, Hindi, Japanese, and Spanish. An internet connection is needed on first run to download multilingual AI models; later runs can be offline. PFS supports OpenAI-compatible inference URLs, including online providers and local engines such as Ollama and LM Studio. The README lists 8 GB of RAM for the core platform and a dedicated GPU, with at least 4 GB of VRAM for smaller models, for local LLM use. The project is licensed under MPL-2.0.