DataMoat
SkillBuilding AI Moats for Everyone
datamoat.org Source★ 30Productivity & Collaboration / Notes & Knowledge Management
- Monthly visits
- <5K
- Sep 2026
- Public revenue
- Undisclosed
- No public revenue evidence yet
- Entry price
- Free
- No monthly subscription pricing
- Launched
- 5mo ago
- 2026-06-21 · Est.
Traffic
Last 12 months
Marketing moves
Launches, search & content, referrals, promotionsX accounts@maxnghello
Launched on Product HuntLaunch
Protect, export, back up, analyze, search, and reuse AI data
Jul 5 · 4mo agoLaunch page
Launched on Fazier (75 upvotes · 22 comments)Launch
DataMoat — Protect, export, back up, analyze, search, and reuse AI data
Jun 21 · 5mo agoLaunch page
Third-party reviews & case dossiers
Acquisition breakdowns, engineer reviews & open-source metricsFazier
2026-06-21Source
- What engineers like:
- 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.
- Best suited for:
- 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.
Open Source & Agent Ecosystem
GitHub · MCP / Skill
Last commit 2026-06-20
- GitHub Stars
- ★ 30
- Forks
- 1
- License
- See repository
- Primary language
- —
Agent, MCP & Developer Integration
Agent Skill- Ecosystem & integrations:
- ChatGPTClaudeClaude CodeCodexCursorDeepSeekQwenOpenClawJetBrains MonoJetBrains
- Exposed tool capabilities:
- Incident analysisOnboardingHandoffFuture model evaluationExportFull coverage
Traffic sources
Sep 2026
Traffic breakdown
Channels
- Direct40%
- Organic Search32%
- Referrals11%
- Social8%
- Other9%
Countries
- Indonesia97%
- Poland3%
Search
Domain authority —Domain registered 2026-04-27
| Keyword | Monthly volume |
|---|---|
| cache ram llama cpp | 290 |
| --cache-ram parameter llama.cpp | 250 |
| llama.cpp kv cache settings | 150 |
| llama cpp cursor | 110 |
Traffic data: Vaneform
Pricing
Pricing page does not list plan prices.
Trial: None · Observed 2026-11-03 · Pricing page
Pages & changes
Tracked since 2026-07-05
Key pages
Weekly snapshots- Homedatamoat.org/
- Pricingdatamoat.org/pricing Snapshot 2026-11-03
- Featuresdatamoat.org/zh-cn/ Snapshot 2026-11-03
Questions & evidence
- How much website traffic does DataMoat get?
- Vaneform estimates <5K website visits in 2026-09. Visits can include repeat visits; this estimate measures web traffic separately from paying customers or revenue.Source
- What is DataMoat and what platforms does it support?
- DataMoat is catalogued under Notes & Knowledge Management: Building AI Moats for Everyone. Supported platforms: Web.
- Is DataMoat open source?
- DataMoat has a public repository (https://github.com/max-ng/datamoat) with its source repository published, recording 30 GitHub stars.Source
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