Data Driven Insights
At KBR, we unlock the full potential of AI and machine learning to transform massive, complex datasets into actionable insights to drive mission success. We use Natural Language Processing, Optical Character Recognition, and Large Language Models to harvest actionable information from millions of text documents and complex engineering drawings. We architect secure scalable data architectures with standardized agent-to-data connectivity (such as Model Context Protocols) to reduce complexity, costs, and security risks. By shrinking analysis cycles from months to minutes and enabling real-time situational awareness, our AI-driven data analytics deliver efficiency, accuracy, and decision advantage for our customers’ most demanding missions.
Sample Use Cases
Double Machine Learning for Causal Analysis
Anomaly Detection
Environmental Impact Analysis
Document Analysis
Double Machine Learning for Causal Analysis
Traditional analytics often stop at correlation, depriving decision-makers of the clarity needed to truly achieve desired outcomes. For complex systems, understanding actual causality is critical to improving reliability and mission readiness. Our Double Machine Learning for Causal Analysis approach applies advanced AI/ML techniques to uncover these causal relationships within large datasets … providing actionable insights about the factors truly impacting system availability. By automating manual processes, KBR has reduced analysis cycles from years to weeks, producing early warnings for potential issues, streamlined workflows, and higher-quality insights that empower smarter decisions and enhance operational efficiency.
Anomaly Detection
KBR is at the forefront of aerospace innovation, deploying AI-powered anomaly detection systems that safeguard complex platforms such as satellites and aircraft. Using deep learning, unsupervised learning, and hybrid modeling, these solutions continuously monitor sensor and operational data to identify operationally significant anomalies that traditional rule-based systems often miss. This technology enhances safety, reliability, and mission success for organizations including NASA, the U.S. Department of Defense, and leading commercial aerospace operators.
Environmental Impact Analysis
Leveraging advanced machine learning and computer vision techniques, we analyze large-scale geospatial and ecological datasets, enabling precise identification and classification of plant communities across diverse landscapes. By combining supervised learning models with automated image recognition, our solutions deliver accurate, scalable insights into vegetation patterns, biodiversity, and ecosystem health. Our AI-drive approach dramatically reduces manual survey time, improves data accuracy, and produces faster environmental assessments, empowering stakeholders to make more informed decisions for conservation, land management, and mission planning.
Document Analysis
KBR has extensive experience applying machine learning (ML), optical character recognition (OCR) and natural language processing (NLP) to extract data and derive actionable insights from millions of government documents. Recent focus areas include Strategic Sourcing insights, Intellectual Property Rights Analysis, and Audit Compliance.
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