What is an AI memory layer for teams?
An AI memory layer for teams is a shared knowledge layer that captures the decisions, context, and patterns from every AI tool session a team runs, then feeds them back into future sessions. Instead of each person and each chat starting from zero, the whole team starts with everything learned so far.
Why per-tool memory is not enough
Per-tool memory is private to one person and one tool. ChatGPT and Claude each have their own built-in memory, but it stays inside that one tool: it never reaches a teammate's work, and it never carries context from one tool into another.
The result is a team where every AI session starts cold. The same background gets re-explained, the same decisions get re-litigated, and the expertise an engineer built up in a session disappears the moment it ends. That is the memory leak: knowledge created in AI sessions that never reaches the rest of the team.
What a team memory layer captures
A team memory layer captures three things: the decisions a team makes, the context behind them, and the recurring patterns in how the team works. It pulls from the places that context actually lives, which is AI coding sessions (Claude Code, Codex), meetings, and documents.
Crucially, it captures this passively. People do not stop to write things down. The memory layer reads the work as it happens, summarizes it, and stores it as searchable team knowledge that any future session can pull from.
How it works: capture, connect, compound
A team memory layer works in three steps: capture, connect, compound. It captures the summary and decisions from each session, connects them so any session can search the team's full history, and compounds because every new session both reads from and adds to the shared memory.
Over time this is the difference between a team that resets to zero every morning and one that gets sharper every week. The memory is the asset, and it grows with use rather than leaking away.
Who an AI memory layer is for
An AI memory layer is for AI-native teams: engineering and product teams that run many AI sessions a day across coding agents like Claude Code and Codex. The more a team leans on AI, the more context it generates, and the more it loses without a shared layer to hold it.
It is self-serve and dev-led. A team installs it, keeps working the way it already works, and the memory builds in the background.
Frequently asked questions
Is an AI memory layer different from Notion?+
Yes. Notion is great for documents a person sits down to write. An AI memory layer captures the ephemeral context inside AI sessions and meetings, the decisions and reasoning that never make it into a doc, and feeds it back to the team's AI tools automatically.
Does it replace ChatGPT or Claude memory?+
No, it complements them. ChatGPT and Claude each have their own built-in memory, but it is private to one person and one tool. A team memory layer sits across every tool and every teammate, so context from one person's session is available to the whole team in the next session, wherever it runs.
Is my team's data private?+
Yes. Wemory comes in two editions. Cloud runs your data in a dedicated, isolated database on managed infrastructure. Self-Hosted keeps the database on your own servers and the data never leaves them. Either way the team controls where its memory lives.
How is it different from a team wiki?+
A wiki only holds what people stop to write, which is a fraction of what a team learns. An AI memory layer captures context automatically from AI sessions, meetings, and documents, stays current without manual upkeep, and is read directly by your AI tools, not just by people.
How much does an AI memory layer cost?+
Wemory is 20 USD per seat per month, with the first month free and no card required. Seats are prorated with no commitment, so a team can add or remove people as it changes. Larger teams and self-hosted deployments are handled as custom plans.