AI for teams
Shared context for AI teams: one memory for people and AI
Shared context for AI teams is what a team and its AI both know and can draw on: its goals, its decisions, who does what, and answers worth keeping. Without it, everyone explains the same background to the AI again and again, and each person gets answers based on what they happened to mention.
Why it matters
Ask a private assistant to draft a plan, and it only knows what you told it in that chat. It does not know the team decided against that approach last month, or that someone else already owns the task. The answer can sound right and still be wrong for your team.
Shared context is what makes multiplayer AI more than a chatbot in a group chat.
What to keep in shared context
| Keep | Example | Why it helps |
|---|---|---|
| Goals | What the team is aiming for this quarter | Answers fit what the team is trying to do |
| Decisions, with reasons | "We chose vendor B because of its support terms" | Settled questions stay settled |
| Who does what | Who owns the monthly report | The AI can point to the right person |
| Good answers | A checked explanation of a process | Nobody has to work it out twice |
What to leave out
- Drafts and guesses: once saved, an early idea can look like a decision.
- Anything only some people should see: shared means shared, so keep salaries, personal matters and restricted client data out, and set AI team permissions for each team.
- Everything else: a team memory full of old chat logs makes the useful parts harder to find.
How to keep it accurate
Shared context can do harm when it is out of date, because a wrong fact gets repeated with confidence. Most of the work is simple upkeep:
- Save on purpose. A person decides what is worth keeping, rather than everything being stored by default.
- Check before saving. Only save an answer someone has read and trusts.
- Record the reason. A decision with its reason can be revisited when things change.
- Remove what has changed. When a decision is reversed, take the old one out or log the new one.
- Name an owner. Someone on the team should look it over now and then.
How SyncMind holds shared context
On the API edition, SyncMind keeps a team's context in a few plain places rather than one big store:
- Team knowledge: save a good answer from a team chat and SyncMind finds it again next time. Saved items can be removed when they stop being true.
- Goals and a decision log: what the team is aiming for and what it has already decided.
- Profiles and roster: who is who, and who owns each recurring task, a person or SyncMind.
Answers in a personal chat cannot be saved to team knowledge, so personal work stays out of the team's shared memory. Together with your files, this lets a team chat with company documents and get answers that fit how it works.
These features are listed for the API edition, where questions go to a model provider. If nothing can leave your building, the on-premise AI assistant has shared team chats, with fewer of these features listed today. The pricing page compares them.
Questions
What is shared context in AI?
Shared context is information a team and its AI can all draw on, such as goals, decisions and useful answers, so nobody has to explain the same thing again in every chat.
How is shared context different from AI memory?
AI memory usually belongs to one person. Shared context belongs to the team, so an answer saved by one person is there for the others.
Do we need a data platform to use shared context?
No. A small team can start with its goals, its decisions and a few answers worth keeping.
Can personal chats add to SyncMind's team knowledge?
No. Only answers from team chats can be saved, so something said in a personal chat does not end up in front of the team.
Which SyncMind edition has this?
Team knowledge, goals, the decision log, profiles and the roster are listed for the API edition.
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