InsightOps

Inspiration

The growing complexity of global conflicts and the vast, untapped potential of social media data inspired us to create InsightOps. Recognizing the need for accurate and reliable information in national security contexts, we were inspired to enhance data processing techniques. While Large Language Models (LLMs) are proficient in extracting meaningful information, they sometimes generate false data, known as "hallucinations," which can be particularly problematic in sensitive security situations. To counteract this, we integrated LLMs with knowledge graphs, which improve data reliability through structural validation and cross-referencing. Additionally, our approach is multimodal, incorporating not only textual but also visual data from social media posts, providing a richer, more contextualized understanding of the data.

What it does

InsightOps utilizes advanced Large Language Models to parse social media content related to geopolitical events, transforming this unstructured data into a coherent, navigable knowledge graph. This allows users to see and explore the interconnections between entities, assess the reliability of information, and gain actionable insights quickly.

How we built it

InsightOps combines modern frontend technologies and a flexible backend for optimal performance. We use Vite and TypeScript for rapid development and type safety, with React for a dynamic UI and React Flow for interactive graph visualizations. On the backend, NetworkX manages our graph data, allowing integration with our data processing workflows.