- 月間訪問数
- <5K
- 2026年9月
- 公開された売上
- 非公開
- 公開収益の証明はまだありません
- 最低料金
- 無料
- 月額サブスク価格なし
- 公開日
- 5ヶ月前
- 2026-06-21 · 推定
訪問
過去12ヶ月
マーケティング動向
公開・検索＆コンテンツ・参照・プロモーションXアカウント@maxnghello
Fazier (75 票 · 22 コメント) でリリース公開
DataMoat — Protect, export, back up, analyze, search, and reuse AI data
6月21日 · 5ヶ月前ローンチページ
第三者レビュー＆事例分析
集客チャネル分析・エンジニア評価・OSS指標Fazier
2026-06-21出典
- 評価されている点：
- 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.
- 適したユーザー：
- 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.
OSS＆Agentエコシステム
GitHub · MCP / Skill
流入元
2026年9月
訪問構成
チャネル
- ダイレクト40%
- 自然検索32%
- 外部参照11%
- SNS8%
- その他9%
国・地域
- インドネシア97%
- ポーランド3%
検索
ドメイン評価 (DR) —ドメイン登録 2026-04-27
| キーワード | 月間検索数 |
|---|---|
| cache ram llama cpp | 290 |
| --cache-ram parameter llama.cpp | 250 |
| llama.cpp kv cache settings | 150 |
| llama cpp cursor | 110 |
トラフィックデータ：Vaneform
料金
料金ページにプラン価格が記載されていません。
無料トライアル：なし · Observed 2026-11-03 · 元の料金ページ
ページ＆変更履歴
2026-07-05 から記録開始
主要ページ
週次スナップショット- トップdatamoat.org/
- 料金datamoat.org/pricing スナップショット 2026-11-03
- 機能datamoat.org/zh-cn/ スナップショット 2026-11-03
よくある質問と根拠データ
- DataMoatのウェブサイトの訪問数は？
- Vaneform estimates <5K website visits in 2026-09. Visits can include repeat visits; this estimate measures web traffic separately from paying customers or revenue.出典
- What is DataMoat and what platforms does it support?
- DataMoat is catalogued under ノート・ナレッジ管理: Building AI Moats for Everyone. Supported platforms: Webのみ.
- Is DataMoat open source?
- DataMoat has a public repository (https://github.com/max-ng/datamoat) ＋ its source repository published, recording 30 GitHub stars.出典
同じカテゴリのプロダクト
生産性・コラボレーション / ノート・ナレッジ管理
同じカテゴリに登録されているプロダクトです。代替として使えるかは、解決する課題、機能、導入条件を比べて判断してください。