Agolo: Enhancing LLM Accuracy with Knowledge Graph-Driven Retrieval Augmented Generation (RAG)

Agolo proposes enhancing the accuracy and effectiveness of Large Language Models (LLMs) through the integration of a Knowledge Graph-driven Retrieval Augmented Generation (RAG) system. This specialized approach allows LLMs to retrieve and generate more precise responses by mapping relationships between entities in large unstructured datasets. Agolo’s platform, which combines advanced AI techniques including Natural Language Processing (NLP) and Machine Learning (ML), extracts entities and relationships from domain-specific content to build a Knowledge Graph that powers contextual intelligence across both defense and commercial applications.In defense settings, the solution improves threat assessments, situational awareness, and early warning detection by reducing LLM hallucinations and enhancing response accuracy with traceable, domain-specific data. The proposed system offers full provenance, ensuring warfighter trust in the AI-generated intelligence. During Phase I, Agolo will validate the use of its Knowledge Graph to support real-time, accurate decision-making and intelligence in high-stakes environments, addressing critical defense needs and contributing to improved readiness to deploy and fight.This technology is adaptable for both defense and commercial markets, offering scalable improvements to AI-powered decision systems, enabling more reliable and actionable intelligence in time-sensitive contexts.