Traditional networking—whether in-person or online—often falls short of delivering meaningful, tailored connections. In physical meetups, people often miss important details about one another, leading to missed opportunities and shallow interactions. Online, generic platforms spam users with attention grabbing and irrelevant messages.

DataBarista tackles these challenges by building its own network—a community knowledge graph that unites private profiles with public, yet anonymous, intents. Here’s how it works:

1. Dual-Layer Knowledge Graph: DataBarista’s network comprises two interconnected layers:
Private Profiles: Privately stored user data in an edge node—including personal background, project details, and sensitive information—accessible only to DataBarista.
Public Intents: Anonymized summaries of users’ professional goals and project challenges that remain verifiable and discoverable by the community public on DKG.

2. Semantic Matchmaking: The matchmaking process is a two-step system:
Rule-Based Filtering: Using SPARQL queries, the system scans the decentralized knowledge graph to retrieve candidate profiles that align with the user’s expertise, project domain, and desired connections.
LLM-Driven Reasoning: A reasoning layer then leverages large language models to analyze these candidates semantically, ensuring that the selected match offers mutual benefits rather than a one-sided connection.

3. Continuous Network Enrichment: By actively listening on platforms like Twitter, DataBarista keeps its community knowledge graph up-to-date, dynamically integrating new insights and ensuring that all matches reflect current expertise and interests.

Overall, DataBarista transforms random networking into a serendipitious and privacy-preserving matchmaking experience that benefits people, agents by making meaningful connections without the creepy data practices.