Data-Driven Insights
KBR harvests actionable information at speed from millions of documents and complex engineering drawings using Natural Language Processing (NLP), Optical Character Recognition (OCR) and Large Language Models (LLMs). We architect secure, scalable data architectures with standardized agent-to-data connectivity, such as Model Context Protocols (MCPs), to reduce complexity, costs and security risks. Our solutions shorten analysis cycles from months to minutes and enable real-time situational awareness.
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 achieve desired outcomes. For complex systems, understanding causality is critical to improving reliability and mission readiness. Our Double Machine Learning for Causal Analysis approach applies advanced AI/Machine Learning (ML) techniques to uncover these causal relationships within large datasets, providing actionable insights about the factors impacting system availability. By automating manual processes, KBR produced early warnings for potential issues, streamlined workflows and offered 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 War and leading commercial aerospace operators.
Environmental Impact Analysis
Leveraging advanced ML and computer vision techniques, we analyze large-scale geospatial and ecological datasets and enable 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-driven 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 ML, OCR and 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.
© 2026 KBR Inc. All rights reserved
Mxxxx-2025-KBR-06-2025