Keep Your AI Context Intact, From One Conversation to the Next

The context you establish in a conversation doesn't transfer anywhere. Your AI doesn't write it down, doesn't remember it, and doesn't carry it forward. Every conversation starts from zero. If you work with AI daily, you're rebuilding context constantly — the same background, the same constraints, the same preferences — at the cost of time you should be spending on the actual work.

Permanent conversation archives — Conversation Memory stores every AI session as a permanent, searchable record. Not just the output — the full exchange, the reasoning, the questions you asked, the answers you got. You can pick up any thread from any past conversation in natural language, instantly.

Stable context that loads automatically — Background Memory holds the facts that don't change: your role, your organization, your project parameters, your preferences. It loads at the start of every session as read-only context. Your AI always knows the basics without you explaining them.

Recurring workflows that don't need re-explaining — Skill Memory stores the processes you run repeatedly: your weekly review format, your standard analysis workflow, your document structure. Load any of them in seconds, in any session, with any model.

1. Connect — Connect MemoryLake to your AI tools via MCP protocol or REST API. Works with ChatGPT, Claude, Gemini, Perplexity, and any LLM accessible via API.
2. Structure — Stable facts go into Background Memory (loads every session). Conversation records go into Conversation Memory (permanent, searchable). Reusable processes go into Skill Memory. Specific facts and decisions go into versioned Fact Memory.
3. Reuse — Open a new conversation with your context already present. Resume a prior conversation without re-reading history. Switch models without losing a thread. Your AI context becomes a persistent asset rather than a temporary state.

Professionals and teams who use AI tools continuously and need their accumulated context to behave like a persistent asset rather than a session-level artifact. This includes knowledge workers who use multiple AI tools interchangeably, teams that need shared AI context, and anyone managing ongoing projects or client relationships with AI assistance.