Distilled Team Memory
Save durable memories that describe the problem, the relevant context, and the takeaway your AI clients should reuse later.
Features
Shared memory for the AI tools your team already uses
ContextVault gives teams a private memory layer for saving proven answers, retrieving them later, and controlling who can access them.
Group-Scoped Access
Separate shared knowledge by workspace group so teams can keep client, project, department, or security-sensitive context in the right place.
Contextual Retrieval
Search blends semantic similarity with text matching so results stay useful even when the question is phrased differently from the original memory.
MCP-Native Workflow
Connect MCP-capable clients to the same vault instead of rebuilding memory separately inside every AI tool your team uses.
How It Works
Store the answer once. Reuse it everywhere.
ContextVault is built around distilled memory, not raw chat transcripts. A memory should capture the useful lesson from a solved problem so another AI session can recover it without dragging the full conversation along.
Each memory can include a problem, context, takeaway, tags, metadata, and group visibility. That structure gives search enough signal to find relevant answers while keeping the stored data compact and reviewable.
Teams can use the same vault from Claude Code, Codex, and other MCP clients while keeping access tied to authenticated users, workspace membership, and group-level permissions.
Built For Shared Workspaces
- Organization-scoped storage keeps tenant data separated.
- Group visibility rules decide which memories each member can search.
- Authenticated MCP access ties every request back to a real user and workspace.
- Feedback signals can be captured now and used to improve ranking later.