What is Memable?
Memable is an AI memory tool that extracts knowledge from your documents, URLs, and files and stores it as persistent AI memory. It does not retrieve document chunks like RAG systems do. Instead, it pulls facts, decisions, preferences, and procedures from your sources and makes them queryable by any connected AI tool.
Most AI tools start every session with no context. Memable builds a persistent memory layer that sits between your knowledge sources and your AI tools. Connect your sources once and the extracted knowledge stays available across every session.
Memable supports both individual and team use. Personal spaces store private AI memory for solo users. Team spaces let multiple users share a common memory pool so everyone works from the same extracted knowledge.
Features & Benefits
- Knowledge extraction — Pulls facts, decisions, preferences, and procedures from connected sources and stores them as discrete, queryable memories
- Source connectors — Ingests knowledge from URLs, file uploads (PDFs, docs, text), and S3 and R2 cloud storage buckets
- MCP server integration — Connects AI memory directly to Claude Desktop, Cursor, and any MCP-compatible tool via a simple config entry
- Personal memory spaces — Private AI memory scoped to a single user, isolated from team data
- Team memory spaces — Shared AI memory pools with seat-based access, keeping distributed teams in sync
- Memory consolidation — Stores extracted knowledge as individual memories rather than large document chunks, reducing context window load
- Source linking — Each memory links back to its original source for traceability
Real-World Applications
Customer support teams that handle recurring issues can connect their ticket history to Memable. When a new ticket arrives, their AI tool queries the shared AI memory and returns the known fix, resolution steps, and any relevant code — without reading through past tickets manually.
Developers working in Cursor or Claude Desktop can feed their documentation, internal wikis, and project notes into Memable. Their tools then carry persistent context across sessions. They no longer need to paste documentation or re-explain architecture each time they open a new chat.
Solo users and freelancers can store client preferences, approved decisions, and workflow notes as AI memory. When returning to a project after weeks away, their AI assistant already has the context. Work stays consistent without manual re-briefing.
Teams in sales, support, or operations can use shared memory spaces to preserve institutional knowledge. New team members access the same accumulated AI memory from day one. When someone leaves, their knowledge stays in the shared pool.
