Miles Jamison 
KBR‘s Mission Technology Solutions business has expanded its partnership with Amazon Web Services to support government agencies in processing large volumes of satellite and sensor data.
What Is the Focus of the KBR-AWS Collaboration?
The effort centers on leveraging artificial intelligence, cloud-based infrastructure and advanced analytics to modernize space and ground systems, KBR said Wednesday. The companies will target faster processing of data from hyperspectral satellites, optical sensors and distributed sensor networks to support satellite monitoring, in-orbit threat detection and Earth observation.
“The ability to process and act on data at speed is fundamental to the next generation of space operations,” said Stephen Chambal, chief technology officer of KBR.
How Does KBR Support System Modernization?
The company’s Speed to Mission Impact framework focuses on transitioning legacy systems to scalable cloud-native architectures. KBR is also using a modular open systems approach to enable more efficient integration of new technologies while maintaining interoperability across platforms. A key objective is to reduce the time between data collection and operational response.
What Investments Support the Effort?
KBR is expanding its AWS-certified personnel and migration capabilities. The company applies AWS-validated methodologies, automation and best practices to reduce risks and costs of cloud transitions while maintaining operational continuity and mission assurance for critical systems. In addition, KBR has earned the AWS Migration Competency recognition, highlighting its capability to support cloud migration efforts.
Previous Efforts in Defense Innovation
In line with this initiative, KBR continues to grow its mission technology portfolio through recent partnerships and contracts. In April 2026, the company teamed with Tagup to advance AI-driven decision intelligence for military operations. Earlier, KBR secured $103 million in task orders supporting U.S. Space Force decision-making and readiness, while also outlining a strategy to scale digital engineering through investments in digital labs and model-based environments.
