DataMoat

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.

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.