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In most AI tools, every conversation lives in the browser tab of the person who started it. When that person is on vacation, rolls off the matter, or simply closes the tab, the context is gone — and the next person has to rebuild the same reasoning from scratch. In professional services, where client work spans weeks or months and teams shift constantly, this is not just inconvenient: it is a hidden cost that compounds across every engagement. ParleHub solves this by attaching context to the project, not the individual.

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How shared context works in ParleHub

Every conversation you have inside a project is stored at the project level and is immediately visible to every project member. When you send a message, your colleagues see it. When the AI responds, everyone on the team can read that response, quote from it, and build on it in the same thread or a new one. There is no “sync” step, no export, and no copy-paste into a shared document. The thread is the shared artifact. This means that the accumulated reasoning, the documents the AI surfaced, the drafts the team iterated on, and the conclusions reached all live in one searchable place for the life of the engagement.
The context belongs to the project, not the person. Leaving a project does not delete your messages — they remain part of the project record, attributed to you by name.

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Onboarding to a project

When you join a project mid-engagement — whether you are a new hire, a lateral joining the matter, or a senior reviewer stepping in for the first time — you do not have to schedule a call to get up to speed. Open the project, scan the conversation list, and read the threads most relevant to your work. The threads show you:
  • What questions the team already asked and how the AI answered them, so you do not re-derive the same analysis.
  • Which documents the AI read and which sections it drew from, so you know what has been reviewed and what has not.
  • Where the team disagreed or iterated, because branches and edit history are preserved in full.
If you still have a question after reading, use semantic search to check whether it was addressed in a conversation you have not found yet before opening a new thread.

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Continuity across team changes

Professional services engagements rarely end with the same team that started them. Associates cycle off, partners step in for closing, contractors join for a specific workstream. In a private-chat model, each departure takes a piece of the context with it. In ParleHub, the thread stays. When a team member rolls off the matter:
  • Their messages remain in the thread, attributed to them by name and timestamp.
  • The AI responses they generated are still searchable and quotable.
  • Any files they uploaded or referenced stay in the project’s file store.
  • Their branches and drafts are preserved exactly where they left them.
The incoming team member reads the thread, gets current, and continues — without a knowledge-transfer meeting and without the risk that something important was only in someone’s personal chat history.

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Best practices

Use a personal chat for exploratory or speculative work — rough drafts, quick experiments, ideas you are not sure will go anywhere. Personal chats are private by design and do not count against the project’s budget or conversation history.Promote a conversation to a project the moment it produces something the broader team needs: a research summary, a draft clause, a risk analysis, a recommendation. Once it matters to the engagement, it belongs in the project where everyone can see and build on it.
Open the project and use the Search bar on the Conversations tab. Type a keyword or describe what you are looking for in plain language — ParleHub’s semantic search will surface relevant messages even if the exact words do not match.If you know roughly which conversation it was in, open that thread and use your browser’s Find (Ctrl+F / Cmd+F) to scan the visible messages quickly. For deeper searches across dozens of conversations, semantic search is faster.
Yes. Every message in a shared project thread shows the author’s name and avatar alongside the timestamp and the AI model used for that response. You can see at a glance whether a message came from a colleague or was the AI’s reply, which model generated it, and exactly when it was sent.This attribution is permanent and cannot be edited. It is part of the engagement record.