U.S. Air Force Adopts MetroStar’s Iris AI Maintenance Platform at Travis AFB

Jul 21, 2026
Travis Air Force Base has acquired MetroStar and ActionStreamer’s Iris platform, an AI system designed for confined-space aircraft maintenance, marking a step toward modernizing Air Force maintenance operations.

Travis Air Force Base in Fairfield, California, has acquired Iris, an artificial intelligence maintenance system developed by MetroStar and ActionStreamer, announced in a press release. The base becomes an early adopter of the technology, which was designed to improve safety and efficiency in confined-space aircraft maintenance.

Iris began as an idea at Royal Air Force Base Mildenhall under the Air Force Spark Tank initiative to improve maintenance of KC-135 aircraft. The system integrates intrinsically safe wearables, two-way audio and video, AI knowledge retrieval, and digital recordkeeping to support technicians working in restricted environments.

At Travis AFB, home to one of the largest air mobility fleets in the United States, Iris will support maintenance on C-5M Super Galaxies, C-17 Globemasters, and KC-46 Pegasus aircraft. MetroStar expects the system to recover more than 35,000 work hours annually and increase aircraft availability by about 7,000 days across the fleet.

The platform is available through the Department of War’s Tradewinds Solutions Marketplace, which provides access to commercial technology for defense applications.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like AI Policy Brief, Defense AI Brief or Daily AI Brief.

Also, consider following us on social media:

AI Safety & Regulation Defense AI Brief AI Brief AI Brief (X)
Free newsletter

Industrial AI Weekly

The latest advancements in smart manufacturing, predictive maintenance, AI-driven quality control, supply chain & more.

AI Token Webinar

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.