Duality AI Unlocks Trusted Synthetic Data for Government-Focused AI & Robotics

Building Physical AI for the Public Sector
AI-driven robotic and autonomous systems are rapidly becoming essential to today’s quickly evolving public sector. From counter-UAS and force protection, to off-road autonomy, disaster response, space operations, and infrastructure inspection, government organizations are increasingly adopting AI models that must safely perceive and act in complex, real-world environments. Successfully building and deploying these systems depends not only on advanced algorithms, but on access to vast volumes of high-quality, trustworthy data for training, testing, and validation.

The Data Challenge
Acquiring real-world data, especially for domains where these models are needed most, is often expensive, dangerous, time-consuming, or simply infeasible. Mission-critical edge cases, such as rare events, extreme environmental conditions, adversarial behavior, or system failures, are precisely the scenarios that matter most, yet are the hardest to capture at sufficient scale. Even when high quality static datasets are available, their usefulness degrades as field realities evolve (i.e. new environment conditions, new adversary tactics, etc.). As a result, teams struggle to obtain the volume, variety, and coverage of data required to train and validate AI systems, resulting in growing timelines, cost overruns, and even stalled projects.

Falcon: Digital Twin Simulation Built for Physical AI
Duality was founded in 2018 with the goal of building a digital twin simulation solution that could accurately simulate complex environmental scenarios along with relevant sensors, and use them to train and test smart systems in real-time.

The result is the Falcon Digital Twin Simulation Platform. Falcon enables engineers to build, train, and test robotic systems and physical AI models with unprecedented realism — accelerating safe, reliable, and scalable deployment in the real world. By combining accurate physics-based simulation, agentic workflows, and Unreal Engine’s photoreal rendering, Falcon enables organizations to bring efficiency and predictability to digital automation by closing the virtual to real gap. By overcoming the limitations of real-world data, Duality is accelerating the path from virtual design to real-world performance, validated in use cases that include counter drone systems, on and off-road autonomy, space robotics, logistics operations, large-scale manufacturing, and more.

Duality’s multidisciplinary team that supports Falcon and our customers includes world-class engineers, simulation specialists, AI/ML experts and award-winning technical artists with over 70 patents across robotics, simulation, and visualization.

Targeted Data Generation and Continuous Improvement
Falcon supports an iterative, closed-loop AI development cycle. Teams can identify performance gaps or failure modes from real-world testing, return to simulation to generate targeted data under precisely controlled conditions, fine-tune models, and validate improvements before redeployment. Built-in ground truth enables rigorous evaluation of detection, tracking, classification, and autonomy performance, while reducing reliance on risky and costly field tests.

This approach has been validated across demanding government use cases, including in counter-UAS work with the U.S. Army XM30 Advanced Capabilities Team.

Trusted Partner for Government AI Programs
Data and operational security are paramount in all AI deployment work where the quality and trustworthiness of training data directly shape how models behave — but it is even more vital in sensitive public sector projects. Duality’s extensive experience working closely with partners in the defense sector, including DARPA, U.S. Army, and NASA JPL, has driven the development of robust safeguards that preserve data integrity throughout the AI lifecycle, and has equipped the team to work with data of varying security levels.