GAAI Cloud Memory
One project memory for all your AI tools.
Your project changes every day. GAAI keeps the context in governed memory, so ChatGPT, Claude, Cursor and your agents can retrieve it when they need it.
For solos, consultants and teams already using ChatGPT, Claude, Cursor or AI agents on active projects.
Your AI provider keys never touch GAAI.
Connect each tool once. Each call is routed to one workspace — not broadcast across all — so Workspace A's context never leaks into Workspace B.
The best AI tool changes. Your project context should not.
Models change fast. AI tools get replaced. Your project context should not disappear with them.
When context stays in old conversations, tool-specific memories and people’s heads, every new AI session rebuilds only a partial version of the project.
Context gets locked in
Useful decisions stay buried in old conversations and tool-specific memories.
Context goes stale
Each tool works from a different version of the project.
Context depends on people
When one person holds the context, everyone waits.
Is this for me?
Two uses. The same need for continuity.
Solo workers
Stop being the context transport layer.
Founders, freelancers, consultants and builders switch between research, writing, coding and planning tools. You reopen a project after a break. You juggle clients or product ideas. The problem is not note-taking; it is making every new AI session understand the same project truth.
- One governed workspace per project or client.
- Decisions and rules survive tool changes and time gaps.
- Your AI can retrieve context without you retyping the backstory.
Switch tools without starting over
Move from research to writing, planning or implementation while keeping the same project memory.
Return from leave faster
Ask for a project recap instead of scrolling old messages, notes and chat history.
Teams
When one person holds the context, everyone waits.
Decisions, rules and operating context should not live in one person's head, inbox, or chat history. Shared memory makes approved project context available to teammates and their AI agents, so fewer answers depend on finding the person who has the context.
- Teammates and compatible AI tools retrieve the same approved project context.
- Fewer emails, messages and meetings just to recover information someone already knows.
- Role-based access keeps sensitive memory scoped.
- Fewer human bottlenecks, less operational friction, and a clearer audit trail of what changed.
Onboarding from day 1
A new teammate can ask their AI agent for approved project decisions, rules and current context.
No more human bottleneck
Teammates' agents query shared memory directly, so the person who remembers is no longer the lookup system.
Handoffs without context loss
When work moves between teammates, their agents can retrieve the accepted project context instead of waiting for a handover.
Want to try it?
Start free with up to 2 seats and 2 project workspaces.
Why it matters
Project-aware context. Grounded agent output.
GAAI keeps useful decisions, rules and constraints in governed memory, so your AI tools can retrieve the right project truth at the right moment without reloading everything into the agent’s context window.
Selected memory
Right context
The agent retrieves the memory that matches the task, not a random slice of chat history.
Right moment
Context is pulled when work resumes, when a question is asked, or when a decision depends on previous constraints.
Agent answer
Answers that fit the project
Answers reflect the project’s decisions, rules and reality instead of generic best practices.
Result
Fewer generic answers. More decisions aligned with the project’s reality.
How it works
Set up your AI tool. Sign in. Manage your project workspaces.
1 · Set up
Copy one MCP URL
Paste the GAAI Cloud MCP URL into ChatGPT, Claude, Cursor or any MCP-compatible agent.
2 · Auth
Sign up or sign in
Your AI tool opens the GAAI auth flow so access is tied to your account, not a copied API key.
3 · Manage
Project workspaces
Create or choose a workspace, then store, govern and retrieve project memory by asking your agent.
Under the hood
How GAAI retrieves the right project memory
GAAI does not rely on one search trick. It combines four retrieval signals, then filters the result through workspace permissions and audit.
Retrieval stack
Example request
“Why did we choose this pricing, and what would break if we changed it?”
Understand the meaning
Semantic search finds the right decision even if you did not use the stored wording.
Keep exact references
Keyword search still catches IDs, function names, rare terms and decision labels.
Rank the answer
Fresh, relevant memory outranks stale context before the result reaches your agent.
Semantic
Find it without the exact words
Ask naturally. The right decision can come back even when your phrasing changed.
Keyword
Never miss the exact term
“DEC-12”, a function name, a domain phrase or an internal label still gets found.
Re-ranked
Best and freshest first
Results are fused and re-ranked so current decisions outrank stale ones.
Graph
See why and what it impacts
Ask about a tracked decision or entity and the relationship graph shows dependencies.
All retrieval runs over a governed, workspace-scoped store, then respects the same permissions and audit model shown below.
Permissions and audit
Shared memory does not mean everyone sees everything.
Teammate A
cleared: Confidential
Teammate B
cleared: Internal
Workspace memory
Confidential
visible to Teammate A
Internal
visible to A and B
Public
visible to all workspace members
Default: Public (level 0). Raise to Internal or Confidential when needed.
Solo workspaces can stay simple. Team workspaces can use member clearance levels when project memory becomes sensitive.
Retrieval follows each member's clearance level — fail-closed, so sensitive memory is denied by default. Every governed read and decision lands on the append-only audit log.
Boundaries that matter
No inference proxy
Your AI tool talks to its provider directly.
No provider keys
Your AI provider keys never touch GAAI.
Workspace scoped
Keep projects or clients separated, one workspace per project, so retrieval stays precise and context never bleeds across clients or projects.
Auditable
Memory changes keep an append-only trail.
Simple pricing for governed memory
LLM inference stays direct between your AI tool and your AI provider.
Plans, seats, workspace limits and founding-cohort details live on the pricing page.
See pricing →Honest answers
- Why not just use my AI tool’s built-in memory?
- Tool memory is useful, but it usually belongs to that tool. GAAI is for project memory that should survive tool changes and stay scoped to a workspace.
- What actually gets stored?
- Project decisions, rules, constraints, summaries, references and operating context that your agents should be able to retrieve later.
- Can I separate clients or projects?
- Yes. Memory is workspace-scoped, so context never bleeds across clients or projects.
- Which tools does it work with?
- MCP-compatible AI tools and agents. See the compatible setup path.
- Does GAAI run my AI model?
- No. Your AI tool talks to its own provider. GAAI is never in the inference path.
- Do you claim token or carbon savings?
- No quantified claim. But pointing an agent to the right governed memory can avoid broad searches across old chats, files and references. Treat that as a practical retrieval benefit, not a measured carbon claim.
Start with one governed project memory.
Keep what the project knows outside the chat, available to the AI tools you already use.