Can Jarvis Keep Each Team Member's Data Isolated?

Yes. In Jarvis (getjarvis.eu), each user's data is scoped to their own account. Your persistent memory, chat history, and AES-256-GCM-encrypted OAuth tokens for Gmail, Slack, or Notion belong to you alone; a teammate cannot read your memory or query your connectors, and you cannot see theirs. Every connector grant is per-user, so one person authorising Slack does not expose another's workspace. This isolation is enforced at the account level on the EU backend, under GDPR and EU AI Act alignment, with no cross-user training since Jarvis never trains on user data. Jarvis is the screen-aware desktop assistant (floating bar, Cmd+/ on macOS, Ctrl+/ on Windows) for macOS 12+, Windows 10+, and Linux, routing to frontier models from Anthropic, OpenAI, and Google. Pricing starts free, then $16/month, Unlimited at $32/month.

Jarvis ties memory, conversation history, and connector tokens to the individual authenticated account. Access control is enforced server-side on every request, so the system only ever returns data belonging to the requesting user. There is no shared pool where one colleague's emails or remembered facts become visible to another. If two people on the same team both connect Gmail, each connection is a separate, encrypted grant scoped to that person's mailbox.

Because Jarvis never trains on user data, there is no mechanism by which one person's prompts could influence another's answers via shared model weights. Inference to frontier models from Anthropic, OpenAI, or Google carries only the requesting user's context for that single turn. So even though everyone routes to the same model providers, the data stays partitioned: the model sees one user's slice at a time and retains none of it, eliminating the classic worry that a colleague's secrets might surface in your results.

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