Conductor OS: Collaborative AI Teaming at Scale

Hardy Dynamics, in collaboration with BigBear.ai (Team HD), proposes to integrate ConductorOS (cOS), an AI/ML autonomy orchestration system, into the US Army Project Linchpin ecosystem. cOS is a critical enabler for AI-based systems, orchestrating and deploying data and AI/ML models across diverse environments. It acts as a conduit for interoperability between disparate autonomous systems, enabling secure, modular connections and facilitating third-party development. By orchestrating AI/ML algorithms across different vendors, cOS transforms data into actionable insights for operators. In essence, cOS empowers Project Linchpin to orchestrate swarms of autonomous and semi-autonomous systems across all domains, even those previously unable to communicate.  cOS achieves this by enabling communication between systems at extended ranges through its vendor-agnostic and third party-focused architecture. It orchestrates third-party AI/ML models and sensors for real-time data fusion and rapid, platform-agnostic deployment. Its open, modular approach allows for the integration of best-of-breed AI models, creating a multi-node system of edge devices that adapt to changing environments, updated models, and DDIL networks. cOS fosters interoperability among industry partners, addresses mission-critical needs, and supports rapid, iterative deployment of trusted AI models across an expanding network of autonomous and semi-autonomous systems.  ConductorOS boasts a TRL7+ maturity level, with successful demonstrations in DoD-sponsored exercises showcasing its AI/ML, data, and sensor orchestration capabilities. By orchestrating unmanned swarms, cOS empowers the Army to achieve more with less, maximizing the benefits of deployed sensors and enhancing human-machine teaming. Its software-based, containerized solution is ready for deployment at the tactical edge, accelerating the Project Linchpin mission and bridging the sensor-to-shooter gap. Centralized AI-enriched information facilitates faster calls for fire and more informed decision-making, ultimately enabling real-time sensor-shooter matching and increased lethality with reduced resource expenditure.