A surprising amount of useful context disappears after a meeting. Notes are saved, emails are scattered, and commitments can easily be forgotten. When the next meeting happens, people often have to search through old conversations to understand what happened before.
We built MEMORA to solve this problem.
Instead of treating meetings as isolated events, MEMORA remembers the relationship over time and brings relevant context back when it is needed.
At the center of MEMORA is relationship memory. By combining AI-powered information extraction, persistent memory, commitment tracking, context retrieval, and meeting preparation, MEMORA turns past conversations into useful context for future interactions.
The Problem We Wanted to Solve
Meeting tools are good at recording information. They can store transcripts, notes, summaries, and action items. The problem is that this information often remains buried inside previous conversations.
Consider a common scenario.
You meet a client and discuss a new product feature.
You agree to:
- Send a prototype
- Complete it by Friday
- Confirm a few requirements
The meeting ends.
A week later, you have another meeting with the same client.
The information already exists somewhere, but finding it requires searching through previous meetings, emails, chats, and notes.
We wanted to build a system capable of remembering these interactions and surfacing the important information at the right moment.
What MEMORA Does
MEMORA is an AI relationship intelligence agent that captures interaction context, stores it as relationship memory, retrieves relevant history, and helps users prepare for future conversations.
At a high level, the workflow looks like this:
text
Meetings / Emails / Chats
↓
AI Extraction
↓
Relationship Memory
↓
Context Retrieval
↓
Meeting Preparation
↓
AI Rehearsal
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