The latest articles on DEV Community by Sathwika Reddy (@sathwika_reddy_029). https://dev.to/sathwika_reddy_029 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4149948%2F8b6d4d4e-3f5c-4f86-8b17-cc5e9f8c9dc3.png https://dev.to/sathwika_reddy_029 en Sathwika Reddy Tue, 29 Sep 2026 14:31:11 +0000 https://dev.to/sathwika_reddy_029/building-memora-an-ai-relationship-intelligence-agent-that-remembers-what-matters-lak https://dev.to/sathwika_reddy_029/building-memora-an-ai-relationship-intelligence-agent-that-remembers-what-matters-lak <p>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.</p> <p>We built MEMORA to solve this problem.</p> <p>Instead of treating meetings as isolated events, MEMORA remembers the relationship over time and brings relevant context back when it is needed.</p> <p>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.</p> <h2> The Problem We Wanted to Solve </h2> <p>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.</p> <p>Consider a common scenario.</p> <p>You meet a client and discuss a new product feature.</p> <p>You agree to:</p> <ul> <li>Send a prototype</li> <li>Complete it by Friday</li> <li>Confirm a few requirements</li> </ul> <p>The meeting ends.</p> <p>A week later, you have another meeting with the same client.</p> <p>The information already exists somewhere, but finding it requires searching through previous meetings, emails, chats, and notes.</p> <p>We wanted to build a system capable of remembering these interactions and surfacing the important information at the right moment.</p> <h2> What MEMORA Does </h2> <p>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.</p> <p>At a high level, the workflow looks like this:</p> <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code> text Meetings / Emails / Chats ↓ AI Extraction ↓ Relationship Memory ↓ Context Retrieval ↓ Meeting Preparation ↓ AI Rehearsal </code></pre> </div> agents ai productivity