Threadly Uses AI and Knowledge Graphs to Manage Professional Relationships

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A developer has built Threadly, a relationship intelligence platform designed to solve the problem of fragmented professional networking data scattered across emails, LinkedIn, and notes. The tool allows users to log networking interactions in plain language, after which its Scanner AI automatically extracts key details such as names, companies, roles, events, and commitments. Threadly stores this information using Neo4j AuraDB, a graph database that models people, companies, and interactions as interconnected nodes rather than isolated spreadsheet rows. Specialized AI agents then analyze the network to surface forgotten connections, recommend follow-ups, and provide insights into a user's career direction. The platform aims to eliminate manual data entry after networking events while giving professionals a scalable, queryable memory of their relationships.

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