Most AI meeting tools are designed to answer one question: What happened in this meeting?
But conversations often contain much more than a summary. They include decisions, assumptions, unresolved questions, project history, and small details that become important weeks later.
A summary captures a moment. Useful memory should help connect moments over time.
The problem with one-off meeting summaries
Imagine working on a project across several lectures, interviews, or team meetings.
After each conversation, you might get a transcript and a summary. But when the next conversation happens, you still have to find the old notes, remember the context, and explain the background to your AI assistant again.
The information exists, but the continuity is missing.
_What if voice notes could become project memory?
_
I've been working on [ViiTor Memo](https://www.viitor.top/), a free AI recording and note-taking product built around this idea.
The goal isn't just to turn speech into text. It's to make information captured from conversations more useful later.
Some of the capabilities we're focusing on:
- Long recording sessions: supports continuous listening/recording for up to 24 hours.
- Meeting notes: organize long conversations into more manageable notes.
- Project memory: connect context across multiple conversations instead of treating every recording as an isolated event.
- MCP integration: make conversation-derived context available to compatible external AI tools and agents.
These use cases may be useful for students attending lectures, people conducting interviews, and teams managing projects across many meetings.
Why MCP matters
An AI assistant becomes more useful when it can work with context beyond the current prompt.
With MCP integration, the idea is to let compatible AI clients and agents access relevant information through a standard integration layer, rather than keeping every note locked inside a single interface.
That raises important product questions, too:
- How should users control which memories an agent can access?
- How do we keep old context from being mistaken for current facts?
- How can users correct inaccurate transcripts or summaries?
- What should happen to sensitive information captured in a conversation?
I don't think long-term memory is just a storage problem. It is also a problem of retrieval, accuracy, privacy, and user control.
I'd love feedback from developers
If you build AI agents or use MCP in your workflow, I'd be interested in your perspective:
- Would access to past meeting and lecture context make your agents more useful?
- How do you manage project memory across multiple conversations today?
- What controls would you expect before giving an agent access to recorded conversations?
You can explore ViiTor Memo here: https://www.viitor.top/
I'm especially interested in feedback on the project-memory and MCP use cases.
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