AI You Can Trust When Failure Isn't an Option
Ad hoc Research Associates LLC is a software and defense engineering practice that develops DarkStax, an operational platform where digital twins, cognitive agents, and governed workflows converge. It provides autonomous decision support, cyber range and adversarial simulation, infrastructure automation, and AI assurance and validation services to help organizations test operational consequences before deploying systems in high-stakes environments.
Builds and maintains living digital twins of missions, networks, infrastructure, and operational systems across mixed-fidelity environments.
Replicates operational and tactical networks in controlled virtual environments to execute mission threads and validate system interactions.
Scales modeling environments from component-level up to enterprise-wide infrastructure.
Deploys cognitive agents that execute workflows, finite state machines, and directed graphs bounded by explicit execution models.
Enables operations teams to build, test, and iterate complex decision flows without deep engineering overhead.
Integrates human-in-the-loop and human-on-the-loop controls to ensure operators remain in command of consequential automated decisions.
Tests AI models inside operational digital twins before deployment to validate behavior against realistic scenarios and adversarial conditions.
Generates synthetic data and provides secure sandbox environments for safe AI experimentation and validation.
Supports the integration, security, governance, and assurance of machine learning into complex operations.
Provides virtual cyber ranges for high-impact training, cyber experimentation, and active scenario simulation.
Provides scenario orchestration and high-fidelity range control for multi-node training exercises.
Pairs range environments with adaptive adversary behaviors that evolve dynamically in response to defender actions.
Provides formal testing and evaluation for complex network-centric C4ISR systems to verify performance metrics.
Measures system performance (MoP) and effectiveness (MoE) utilizing IP packet data analysis.
Enables distributed real-time, multi-node network emulation scenarios using commodity hardware.
Defense · Telecommunications · Government
Defense · Client: Defense Cyber Training Organization
Delivered a high-fidelity digital twin and low-code scenario orchestration solution that reduced setup times and provided white cell operators with real-time control over red, blue, and grey activity during training exercises.
Defense · Client: U.S. Air Force Cyber Stakeholder
Developed an adaptive adversary simulation within a realistic range environment. The solution replaced static attack playbooks with evolving tactics that react dynamically to defender actions.
Defense · Client: Army C5ISR Laboratory
Replicated complex tactical and C2 networks in a controlled digital twin environment, enabling multi-node test scenarios, system interaction validation, and mission thread execution before deployment.
Defense · Client: Army AI Innovation Stakeholder
Created a secure AI sandbox to generate synthetic data, test model behavior, and validate AI-driven workflows without exposing sensitive operational systems.