This talk presents a three-step process that combines knowledge graphs with large language models (LLMs) to revolutionize how law enforcement agencies gather, analyze, and share criminal intelligence. This approach addresses critical challenges in modern policing: data silos, investigation complexity, and the need for transparent, explainable intelligence sharing.

Like brilliant but untamed minds, agentic applications in production present a unique challenge: they solve problems in revolutionary ways but can be wildly unpredictable. The art of deploying these free spirits requires a delicate balance between autonomy and reliability. At Orra, we've developed a "glue layer" that acts as a skilled wrangler, ensuring reliability while preserving the agents' freedom in production environments. We'll explore its architecture including our approach to adaptive execution planning, and how we enhance domain understanding.

Whenever you read about taking Retrieval Augmented Generation beyond simple vector search on embeddings, graphs are almost sure to come up. But what graphs? Old-school knowledge graphs, with entities and their relationships, or document-centric graphs, with text snippets as nodes? And how do you use them to improve your retrieval? Nearest neighborhood? PageRank? Something else? I will provide an overview of what's happening in that space, including what I'm doing, and give you a tour of the different options, with their pros and cons.