M. Edwards and D. Rogers
DARE Labs, Georgia, United States
Keywords: Artificial Intelligence, Decision Support, Data Fusion, Knowledge Graph, Reinforcement Learning
Cerebro, developed by DARE Labs, transforms fragmented structured and unstructured data into the training and evaluation resources required to build mission-relevant AI systems. The platform first ingests information from disconnected sources and organizes it within a typed knowledge graph. This graph identifies entities, relationships, and operational context, creating a consistent representation of how people, assets, documents, sensors, events, and outcomes are connected. Cerebro then uses this connected representation to generate machine learning datasets in which features, targets, and training examples preserve the context and relationships found across source systems. The same data can be structured into reinforcement learning environments that represent operational states, available actions, expected outcomes, reward criteria, and model evaluation rules. This allows organizations to train and test AI systems against realistic workflows instead of isolated data points. The knowledge graph also provides a visual layer through which users can inspect source information, explore relationships, review generated datasets, and understand the context used to evaluate model behavior. This poster demonstrates a unified pipeline from fragmented data to connected knowledge, AI-ready datasets, reinforcement learning environments, and explainable decision support for defense and dual-use applications.