Juba
AI-Powered Investigative Knowledge Graph
Challenge
Visualizing complex relationships between entities (people, companies, events) requires intuitive graph rendering while maintaining performance with large datasets. Building an AI research agent that can autonomously discover connections adds another layer of complexity.
Solution
Juba maps networks with an interactive force-directed graph. The Research Agent uses leading AI models with tool calling to discover entity connections on its own. Session fingerprinting and encryption protect sensitive investigations across multiple layers. The crime board aesthetic ties the pieces together with pinned cards and connecting threads.
Results
- Interactive force-directed graph visualization
- AI Research Agent with autonomous tool calling
- Entity profiles for persons, companies, events
- Timeline view with chronological event tracking
- WCAG 2.1 AA accessibility compliance
- Multi-layer session security and encryption
System Architecture
Investigative journalism platform with AI research agent and network visualization
Investigative journalism platform with AI research agent and network visualization
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