- 每月造訪次數
- <5K
- 2026 年 9 月
- 公開營收
- 未公開
- 暫無公開營收證據
- 入門價
- 免費
- 沒有月付訂閱價
- 首次發布
- 5 個月前
- 2026-06-21 · 估算
訪問
近 12 個月
行銷動作
發布、搜尋與內容、引薦、促銷X 帳號@maxnghello
在 Fazier 發布（75 票 · 22 評論）發布
DataMoat — Protect, export, back up, analyze, search, and reuse AI data
6 月 21 日 · 5 個月前發布頁
第三方評測與案例拆解
客戶來源管道拆解、工程師評測與開源檔案Fazier
2026-06-21查看原文
- 核心亮點評測：
- Raw records first Original source records, ChatGPT export files, session JSONL, logs, metadata, skills snapshots, and attachments are preserved when the source provides them, then stored inside the encrypted memory archive. Prompt-to-output trail Normalized records keep prompts, responses, tool use, tool results, timestamps, model metadata, source path, and stable attachment links together so the surrounding work can be reviewed later. Local private custody The memory archive, search data, skills, attachments, and memory encryption keys stay on-device. DataMoat is not a platform-account personalization layer or transcript cloud. Cross-provider layer Supported ChatGPT exports, Claude, Codex, Cursor, DeepSeek/Qwen local workflows, OpenClaw, skills, and attachments land in the same local memory layer instead of staying trapped inside one vendor view. Company AI work asset Work history becomes a protected company or personal asset for review, incident analysis, onboarding, handoff, private memory, and future model evaluation under your own rules. Portable memory ownership If the team changes tools or models, the memory layer can move with the encrypted DataMoat folder. Vendor choice changes; custody of the work record stays yours. Black box Reconstruct what happened when an AI-assisted task mattered. Knowledge base Keep the process, not only the final answer or commit. Handoff layer Give future teammates and agents the decisions behind the work. Private memory Build reusable context without sending a memory archive to DataMoat.
- 適用人群：
- 01 / problem Who solved what? Keep the work record around incidents, migrations, bugs, product decisions, and repeated workflows so the next person can find the path, not just the outcome. 02 / context What did the AI see? Preserve supported file context, attachments, skills, metadata, local source records, and the surrounding session that shaped the answer. 03 / prompts How did employees prompt? Save the prompt trail, corrections, constraints, clarifications, and decisions that turned a vague task into a usable result. 04 / tools Which tools ran? Capture supported tool calls, command output, errors, timestamps, source metadata, and stable attachments when the source writes them locally. 05 / decision Which solution shipped? Keep the evidence around the adopted approach: alternatives discussed, failed paths, final commands, review notes, and the reason the team moved forward. 06 / reuse Can it be reused later? Make AI work searchable for reuse, audit, incident review, onboarding, project handoff, and private AI memory across ChatGPT, Claude, Codex, Cursor, DeepSeek, Qwen, and OpenClaw workflows.
開源倉庫與 Agent 生態
GitHub 開源與智慧體接入
Agent 智慧體、MCP 與開發者接入
Agent Skill流量來源
2026 年 9 月
訪問構成
管道
- 直接訪問40%
- 自然搜尋32%
- 外部引薦11%
- 社交媒體8%
- 其他9%
國家/地區
- 印尼97%
- 波蘭3%
搜尋
站點權重 —網域名稱註冊 2026-04-27
| 關鍵字 | 月搜尋量 |
|---|---|
| cache ram llama cpp | 290 |
| --cache-ram parameter llama.cpp | 250 |
| llama.cpp kv cache settings | 150 |
| llama cpp cursor | 110 |
流量資料：Vaneform
定價
定價頁沒有列出方案價格。
試用：未提供 · 觀察於 2026-11-03 · 原定價頁
頁面與變化
開始記錄於 2026-07-05
關鍵頁面
每週快照- 首頁datamoat.org/
- 定價datamoat.org/pricing 快照 2026-11-03
- 功能datamoat.org/zh-cn/ 快照 2026-11-03
問題與證據
- DataMoat 的網站造訪次數是多少？
- Vaneform 估算 2026-09 網站造訪次數為 <5K。造訪次數包含重複訪問，付費客戶數與收入需分別查看對應證據。來源
- DataMoat 是什麼產品，支援哪些平台？
- DataMoat 歸屬「筆記與知識管理」市場領域：DataMoat 是面向個人和團隊的本地 AI 工作空間，可加密儲存、搜尋、備份和複用多種 AI 工具的工作記錄。。 支援平台包括：僅網頁。
- DataMoat 是開源專案嗎？採用什麼協議？
- DataMoat 擁有公開程式碼倉庫（https://github.com/max-ng/datamoat），已公開原始碼倉庫，記錄有 30 個 GitHub Stars。來源
同分類研究對象
效率與協作 / 筆記與知識管理
這些產品屬於同一目錄分類。選型時請進一步核對客戶任務、功能與部署要求。