Keep Team Knowledge and AI Work Together With TeamBrain

AI can help one person finish a task faster. Teamwork becomes harder when everyone uses it separately.

One person has useful research inside ChatGPT. Another has a draft in Claude. Project decisions are spread across Slack, documents, and calls. When someone new joins the work, they need the same files and explanations before their AI can help.

TeamBrain gives the team and its connected AI tools one shared place for documents, tasks, decisions, comments, and project context.

People can continue working with the AI tools they already use. Their AI reads from and writes to TeamBrain, so useful work does not have to remain inside one person’s chat history.

AI Work Breaks Down When Context Stays With One Person

Most AI tools begin with an individual conversation.

You upload a document, explain the project, correct the first answer, and add details the AI could not know. By the end of the conversation, the chat contains useful context about the task and the decisions made along the way.

The next person does not automatically get that context. They may have the final file, but not the reasoning behind it. Their AI starts from another blank chat and asks for the same information again.

Teams often work around this by copying prompts, forwarding chat links, uploading the same files, or explaining the project in another meeting. That creates several versions of the same knowledge, and none is clearly the current one.

TeamBrain puts the shared context between the people and AI tools working on the project.

What Is TeamBrain

TeamBrain is a shared workspace for teams that use AI.

It stores documents, briefs, notes, tasks, comments, decisions, assets, and other project information. The same workspace can be accessed by team members and by AI clients connected through MCP or TeamBrain’s API.

TeamBrain is not meant to replace Claude, ChatGPT, or another AI chat tool. People keep using their preferred AI and connect it to the shared workspace.

A team member can ask their AI to retrieve the latest client information, save a new draft, update a task, or add a note to the correct project. Another person’s connected AI can then work from that updated information.

The product also includes project management features, so the documents and the work connected to them can stay in the same place.

Build the Shared Brain From Existing Work

The TeamBrain workflow begins with the information the team already has.

You can add documents, briefs, notes, deliverables, and other files to a workspace. These files become the source that team members and connected AI tools work from.

The workspace can be arranged around a project, client, department, campaign, or another part of the business. This allows a company to separate information instead of placing every document and task into one large collection.

When the team creates new work, it can be saved back to the relevant workspace. A draft produced through one person’s AI then becomes available to the rest of the people who have access.

The usefulness of the shared brain still depends on what the team puts into it. Old files, incomplete briefs, or conflicting instructions can give people and AI the wrong context. Someone needs to keep the source material current and remove information that should no longer guide the work.

Keep Using Claude ChatGPT and Other AI Clients

TeamBrain connects to AI clients through the Model Context Protocol, commonly called MCP. It also supports connections through its API.

The company lists Claude, ChatGPT, and other major AI clients as supported options. Any AI client that supports MCP can connect directly to the shared workspace.

The setup involves adding a connector inside the AI client, copying the TeamBrain address from the Connect page, and approving access. TeamBrain describes this as a one-time setup rather than a development project.

After connecting, people continue giving instructions in their usual AI tool. They can ask it to read a project brief, find the latest decision, create a document, or save completed work into TeamBrain.

Model support can change as AI clients update their connection options. Teams should check the current setup guide for the exact client and account they plan to use.

Store Team Knowledge as Markdown

TeamBrain stores its core knowledge as plain Markdown.

Markdown keeps documents in structured text rather than a proprietary page format. AI tools can read and write the content without working through a visual page builder, while people can still follow the headings, lists, and written information.

This structure also supports data portability. TeamBrain provides one-click Markdown export, allowing a team to download its documents instead of leaving them inside a format that only TeamBrain can open.

Markdown works well for briefs, notes, decisions, documentation, and written project material. Files and other assets can be attached to the work they belong to.

The format does not make every document accurate by itself. Team members still need to review what an AI writes before it becomes part of the shared source.

Keep the Reasoning Behind Project Decisions

A final document does not always explain why the team chose one direction over another.

A client may have rejected an earlier concept. A deadline may have changed because another task became more important. The team may have removed a feature after discussing cost or technical limits.

TeamBrain includes a decision log that records changes with the reasoning behind them. Its activity and task history can show what changed, who made the update, and why the decision was made.

This gives the next person more than the latest file. They can see the context that shaped it and avoid reopening a question the team already settled.

Connected AI tools can also use that history when working on the next task. A new draft can follow an earlier decision without requiring someone to paste the same explanation into another prompt.

The decision record is only useful when the reasoning is captured clearly. Important client, financial, legal, or product decisions should still be reviewed by the responsible person before they become the basis for later AI work.

Manage Tasks Alongside Documents

TeamBrain includes project management features inside the same workspace.

The Kanban board tracks work from to-do to done. Tasks can also be shown in a list view with the due date, status, and assignee in sortable columns.

A shared calendar shows deadlines and events. Comments remain attached to documents, while submissions and status changes can appear in the task discussion. Assets and completed deliverables stay with the work they relate to.

The dashboard gives the team a view of what is moving and what is blocked. TeamBrain’s AI can create or update tasks and summarize project status from the information in the workspace.

This reduces the need to keep project context in one tool and task status in another. It does not remove the need for ownership. The team still needs to assign the right person, confirm deadlines, and review AI-created task details.

Organize Work With Workspaces and Collections

Different teams need different boundaries around their work.

An agency may create one workspace for each client. A startup could separate product, marketing, operations, and fundraising. A consultant may keep each engagement in its own space.

TeamBrain supports workspaces organized by project, client, department, or another structure. Access can be limited so people only see the spaces they should use.

Collections provide another way to group related documents, tasks, and assets. A collection could hold everything connected to a campaign, product launch, or client deliverable without mixing it with unrelated work.

Clear organization matters because connected AI tools rely on the same structure. A well-named workspace and collection make it easier to retrieve the right context and reduce the chance of placing work in the wrong location.

Invite Clients and Contractors Without Adding Full Seats

TeamBrain separates team seats from guest accounts.

Seats are intended for people inside the team. Guests are designed for clients, contractors, and other outside collaborators who only need access to selected workspaces.

The pricing page says guests can read and comment on the work without taking a full team seat. Their access is limited to the workspaces where they have been invited.

This can help an agency share a client project without exposing its internal work or other client accounts. A company can also invite a contractor to the material needed for one assignment.

Workspace owners should still review permissions before inviting someone. A connected AI may transmit relevant workspace content to the AI provider when a user requests an action, so access should match what that person is allowed to use.

Start the Day With the Current Priorities

TeamBrain sends each teammate a morning email showing what is on their plate and what changed.

The summary is built from tasks and project activity inside the workspace. It can surface current assignments, recent updates, and work that needs attention without requiring the team to collect the information manually each morning.

The dashboard provides a broader view of active and blocked work. A reviewer can also receive an email when someone submits work for review.

These notifications can reduce status questions, but they depend on the workspace being current. An unassigned task or missing deadline cannot appear correctly in a daily summary.

Teams should also decide which updates need a direct conversation. A morning email can show that a decision changed, but sensitive or high-impact changes may still need a call or written approval from the responsible person.

Understand How TeamBrain Handles Data

TeamBrain says Customer Content is private to the team, protected by workspace access controls, and encrypted in transit. Its privacy policy states that Customer Content is not used to train AI or machine-learning models.

The policy also explains what happens when a connected AI reads or writes to the workspace. The relevant content is transmitted to that AI provider so it can complete the requested action. The provider then handles the content under its own terms and privacy policy.

Teams using their own API keys are responsible for the connected accounts and usage. Content processed by TeamBrain’s included AI is used to perform the requested action and, according to the company, is not used to train a model on Customer Content.

TeamBrain stores and processes data in the United States. Companies with client contracts, data residency rules, or compliance requirements should check whether that location and the policies of connected AI providers fit their needs.

Deleted items stay in trash for 30 days before permanent removal. Users can also export their documents as Markdown and request account deletion.

Choose Between Included AI and Your Own Model

Every TeamBrain plan includes access to an open model run by the company. TeamBrain describes it as suitable for everyday work such as drafting, summarizing, and filling gaps.

Teams can also bring their own AI key and connect a supported model. This gives them control over the provider, model quality, usage volume, and charges on that account.

The amount of included AI differs between Starter, Pro, and Max. The public pricing page does not list the exact usage allowance for each plan, so teams that expect heavy usage should confirm the current limits before choosing one.

Bringing your own key does not remove the need to check the AI provider’s data rules and billing. The requests run through that provider and use the connected account.

TeamBrain Pricing

TeamBrain uses plan limits based on seats, workspaces, guests, storage, and included AI rather than charging for every added person without a limit.

Starter costs $12 per month or $10 per month when billed annually. It includes five seats, five workspaces, 50 guests, and 10 GB of storage.

Pro costs $24 per month or $20 per month when billed annually. It includes 20 seats, 20 workspaces, 250 guests, and 75 GB of storage.

Max costs $49 per month or about $40.83 per month when billed annually. It includes 50 seats, 75 workspaces, 1,000 guests, and 200 GB of storage.

Every plan lists included AI and the option to bring your own key. Larger teams that need SAML single sign-on or an admin audit trail must contact TeamBrain for a custom arrangement.

Pricing and launch offers can change, so teams should check the current plan page before purchasing.

Who Is TeamBrain For

TeamBrain is designed for teams that already use AI and need a shared source for the work created around it.

Founders and small companies can keep company notes, tasks, decisions, and project documents available to the whole team. Agencies can separate client work into different spaces and invite clients or contractors as guests.

Consultants and freelancers can organize several engagements while keeping briefs, deliverables, and decision history together. Remote teams can use the shared context to continue work across time zones without repeating every update.

It will be less useful for someone who only needs a private AI chat or a basic file drive. TeamBrain becomes more relevant when several people and their AI tools need to read from and contribute to the same working context.

Put Shared Context Between Your Team and Its AI

TeamBrain gives people and connected AI tools one place to find documents, tasks, decisions, assets, and the reasoning behind the work.

The team keeps using Claude, ChatGPT, or another supported AI client. TeamBrain provides the shared layer where completed work and project context can be stored for the next person or AI that needs it.

You can begin with one project, add the source documents, connect an AI client, and invite the people who need access. Before using it for sensitive work, review the workspace permissions, connected provider policies, data location, and plan limits that apply to your team.