Shield AI

Shield AI is an artificial intelligence and autonomy company founded in 2015 in San Diego California by Andrew Reiter Brandon Tseng and Ryan Tseng. The company is known for its Hivemind AI software developed for autonomous aerial vehicles and flight systems. Shield AI has offices in countries including the United States the United Arab Emirates Ukraine and Australia and provides AI-enabled solutions for military operations conducted by allied nations.

Founding

Shield AI was established to develop autonomous systems for military operations. The company’s Hivemind software enables fighter jets and unmanned aerial vehicles (UAVs) to perform missions independently. In addition to a $200 million investment received in October 2023 Shield AI raised an additional $240 million in March 2025 through its Series F funding round with participation from L3Harris Hanwha Aerospace Andreessen Horowitz and other investors. Following this investment the company’s total funding reached $1.3 billion and its valuation rose to $5.3 billion.

Hivemind Autonomy Platform

Hivemind is an AI-based autonomy software developed by Shield AI. The platform is designed to enable autonomous mission execution on air land and sea vehicles without human intervention. Hivemind can perform functions such as mission planning environmental perception route determination decision making and post-mission analysis. The platform features a robust architecture capable of operating even in environments where GPS or communication links are disrupted.

The Hivemind software is integrated into all systems developed by Shield AI and consists of the following components:

EdgeOS: A low-latency real-time modular and scalable software infrastructure designed for autonomous systems.

Pilot: An autonomy module that incorporates perceptual and cognitive capabilities such as situational awareness mapping target tracking and mission planning.

Forge: A software factory developed for simulation testing analysis and certification processes. It enables developers to rapidly deploy and validate autonomy solutions.

Commander: The user interface and control layer that facilitates human-machine interaction. It allows operators to plan missions monitor operations and manage tasks.

V-BAT Unmanned Aerial Vehicle

V-BAT is Shield AI’s long-range unmanned aerial vehicle system featuring vertical takeoff and landing (VTOL^【1】). Equipped with the Hivemind software V-BAT can operate autonomously even in environments with electronic warfare threats and without GPS or communication links. It was developed for missions including reconnaissance surveillance intelligence (ISR) border security and maritime patrol. The system is configured to carry synthetic aperture radar (SAR) electronic warfare payloads ViDAR sensors SATCOM links and various other mission-specific payloads.

The updated V-BAT version introduced in 2025 stands out with higher autonomy levels and enhanced BLOS^【2】 capabilities. The system has been deployed on numerous vessels of the United States Navy and has been adopted by the United States Coast Guard and the Japan Maritime Self-Defense Force as an intelligence and reconnaissance platform.

ViDAR Observation Pod

ViDAR (Visual Detection and Ranging) is an AI-enabled passive surveillance and target detection system developed by Shield AI. Designed for land and maritime missions the system provides wide-area continuous monitoring. The ViDAR Pod includes multi-spectral cameras an integrated processor and an inertial measurement unit. This enables the system to detect both stationary and moving targets under day and night conditions.

Integrable with Group 3 class UAVs ViDAR offers a passive surveillance alternative to radar systems and provides a low-emission operational solution. Thanks to its advanced analytical capabilities it can be applied in diverse scenarios ranging from illegal fishing detection to search and rescue operations.

International Collaborations

Shield AI has drawn attention through its activities in Ukraine. The company’s Kiev-based team has conducted over 130 missions alongside Ukraine’s Armed Forces Unmanned Systems Forces using V-BAT systems. Shield AI also trains Ukrainian veterans and employs them as operational personnel.

In 2025 Shield AI signed a collaboration agreement with Airbus U.S. Space & Defense. Under this agreement Shield AI’s Hivemind software will be integrated onto the MQ-72C Logistics Connector the unmanned variant of the UH-72 Lakota helicopter for use in the United States Marine Corps’ Aerial Logistics Connector (ALC) program.

Technological Advancements

In April 2025 Shield AI acquired the “Data Driven Readiness (DDR)” software from Crowdbotics. DDR is a software that analyzes flight data to assess pilot proficiency and has been integrated into Shield AI’s Hivemind software to evaluate autonomy performance. This acquisition is part of Shield AI’s strategy to enhance its post-mission analysis and continuous performance improvement capabilities for autonomous systems.

Future Vision

Shield AI’s long-term vision is to make AI-assisted autonomous pilots a fundamental component of future military operations. According to co-founder Brandon Tseng by the 2030s every military platform—including aircraft drones ships satellites and submarines—will be operated by an AI pilot. In this context the company aims to conduct autonomous operations across all air land and sea platforms leveraging the modularity and scalability of the Hivemind platform.

Going forward Shield AI continues to focus on certifying integrating and field-testing autonomy solutions that support human-machine collaboration. A key operational priority for the company is validating its systems not only in laboratory and test environments but also under real combat conditions. Additionally the company plans to develop strategic partnerships with defense forces of various nations to deliver global autonomy solutions and broadly integrate the Hivemind infrastructure with third-party platforms.

Shield AI’s future initiatives encompass not only developing new products but also accelerating learning cycles within existing systems simplifying user interaction and expanding software production lines to deliver operational solutions to the field more rapidly. In pursuit of these goals components such as Forge Commander Pilot and EdgeOS are continuously updated with tighter integration among AI simulation testing and analysis processes.