A generative AI system is only as effective as the quality and breadth of the data it has access to. Its ability to generate accurate, relevant, and contextually appropriate responses is directly tied to the reliability of its data. If the data is incomplete, outdated, or biased, the AI’s outputs will reflect those limitations. Ensuring access to well-structured, reliable, and diverse data is critical for maximizing AI performance and delivering trusted results. Even the most advanced AI models cannot consistently produce accurate and useful insights without a strong foundation of high-quality data.
In national defense, the stakes for accuracy and transparency in AI-generated responses are especially high. Decision-makers rely on AI to provide timely, precise, and actionable insights, often in complex and high-pressure situations. Misrepresentation or inaccuracy in the data could lead to critical errors with far-reaching consequences. Therefore, access to verified, trustworthy data is crucial for AI in defense applications. Additionally, transparency is essential to ensure the reasoning behind AI-generated insights is traceable and understandable. A system grounded in accurate data and capable of producing clear, explainable outputs fosters trust, supports informed decision-making, and reduces risks in sensitive national security contexts.
3GIMBALS leverages GraphRAG—a combination of Knowledge Graphs and Retrieval-Augmented Generation (RAG) techniques—to optimize information retrieval by its OMEN AI system. GraphRAG integrates structured data from knowledge graphs with advanced RAG search capabilities, enabling OMEN to ground its outputs in verified, interconnected data. This approach allows OMEN to retrieve information based not just on keyword matches but through an understanding of the relationships between entities, ensuring precise, context-aware responses. The transparency of GraphRAG further allows users to trace how the AI arrives at its insights, delivering explainable and trustworthy outcomes essential for high-stakes defense operations.
Our knowledge graph works in both resource description framework (RDF) and property graph formats, each offering distinct advantages. RDF excels in environments that require interoperability and precise semantic understanding, as it allows for the seamless merging of datasets from different sources while maintaining consistency. It is ideal for representing highly interconnected, semantic data with strict adherence to ontologies and standards. On the other hand, the property graph format is optimized for flexibility and performance, enabling faster querying and traversal of large-scale data networks. This format is particularly useful for real-world scenarios that require dynamic, evolving data structures and complex querying, such as real-time decision-making processes. By supporting both formats, 3GIMBALS ensures that OMEN can handle diverse data requirements with efficiency and precision.
Our OMEN GraphRAG system powers chat interfaces that convert natural language prompts into structured queries and vectorized similarity retrieval, optimizing responses to both open-ended and highly specific questions. The OMEN chat interface allows users to see exactly how the AI system retrieves information, providing immediate sourcing for its responses, ensuring transparency and trust in the insights it delivers.
