Mission Brief
Founded: 2015
Headquarters: San Diego, California
Offices: Dallas, Washington DC, and International Hubs
Mission: Shield AI’s mission is to protect service members and civilians with intelligent systems.
Core Technology: Shield AI’s flagship product, Hivemind, is an artificial intelligence pilot that has flown multiple platforms, including the F-16 fighter jet, the V-BAT vertical takeoff and landing drone, MQ-20 aircraft, and the MQM-178 Firejet.
Key Customers: U.S. Department of Defense and Allied Forces
Operational Focus: Shield AI’s systems are designed for contested environments; enabling autonomous systems to perform ISR (Intelligence, Surveillance, Reconnaissance), tracking, and targeting in dynamic, high-stakes missions.
“Delivering V-BATs at scale requires eliminating any bottlenecks from test, validation, and delivery. Nominal helps us move from raw vehicle data to high confidence decisions, without losing time.”
— Brandon Tseng, Co-Founder, President
The Challenge
“Speed of iteration is what matters in fielding new capability. The pace of testing - reviewing data and making decisions - defines everything else we can do. We had to solve this. Nominal has helped us deliver on both speed and validation.”
— Armor Harris, SVP Aircraft
Shield AI operates in a world where test cycle times determine the speed at which they can deliver intelligent, affordable mass to the warfighter. V-BATs, powered by Hivemind, must be able to operate autonomously in communications- and GPS-denied and degraded environments such as Ukraine and the Indopacific. Such missions often rely on a fast feedback loop to test and perfect flight data in order to get back into the fight. The faster Shield AI is able to iterate on this loop, the faster V-BAT can contribute to the mission.
To meet these mission goals, Shield engineers have to:
Capture and manage the increasing volume of test data without losing track of critical details.
Eliminate delays between testing and analysis by removing unnecessary steps.
Develop data infrastructure and tools that work in low-connectivity environments where traditional tools fail.
Without these capabilities, engineering teams face wasted time, missed opportunities, and stalled progress – which can, sometimes, mean the difference between enabling a mission, sitting this one out, or even crashing.
The Solution: Real-Time V-BAT Flight Testing
Analysis Velocity
Nominal enables Shield AI to review V-BAT test data before engines cool. Data flows directly from the drone into Nominal’s platform, removing the need for engineers to stitch together files manually. Engineers can optimize their minutes-on-mission by generating live plots comparing multiple flights, annotating findings, and diagnosing root causes of issues in minutes – not hours.
Low-Connectivity Field Support
Nominal + Shield AI’s elite engineering teams co-built field tools for limited connectivity, ensuring Shield AI’s teams can capture, store, and analyze data wherever V-BAT’s missions take place – whether in the Gulf of Mexico interdicting drugrunners or in Ukraine searching for missile silos. This capability has proven essential for V-BAT testing both at home in the Dallas, TX manufacturing facility or in remote or contested areas.
Data Catalog for Future Missions
V-BAT testing generates massive amounts of telemetry, video, and environmental data across simulations, HITL tests, and field operations. Nominal’s data infrastructure indexes and synchronizes every frame, providing Shield AI with a durable system for current and future platforms.
The Results
Shield AI’s engineers use Nominal to:
Reduce delays between tests and decisions.
Manage and process large volumes of data without adding complexity.
Test and analyze systems in field conditions that demand reliability.
“Within 5-10 minutes of data being uploaded to Nominal, we were able to eliminate and hone in on potential reasons for a flight issue because multiple teams reviewed their specific data. Typically, it would have taken days for a team to do a deep dive review with their own MATLAB scripts to determine what went wrong. Nominal is already 1 to 2 orders of magnitude better than previous processes.” — Austin Howard, Engineering Fellow
This allows Shield AI to deliver resilient, mission critical systems at full throttle.
Looking Ahead
Testing isn’t just part of the process—it’s the engine that drives progress. Shield AI and Nominal continue to deepen their partnership, ensuring the tools evolve as quickly as the mission requires.