FoxTrot Search vs Precision File Search
FoxTrot Search
foxtrot-search.comPrecision File Search
pfs-ai.github.ioSide by side
| Figure | FoxTrot Search | Precision File Search |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | — | Yes |
| iOS | Yes | — |
| Plans published | 4 | 1 |
| Spec score · Desktop Search Software | 8.0 (#11) | 7.6 (#23) |
| Security | The maker's Privacy and Security Policy says document content and extracted data are not sent to CTM or third parties during or after indexing. | The maker lists path-traversal prevention, XSS sanitization, and prompt-injection hardening as security measures. |
Plans
FoxTrot Search
- FoxTrot Personal Search$35, paid once
- FoxTrot Professional Search$120, paid once
- FoxTrot Professional Search 5-user$240, paid once
- FoxTrot Search Server 10-user$480, paid once
In their own words
FoxTrot Search
FoxTrot Search has no write-up on Specifiction yet; its figures are above.
Precision File Search
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.