Electric wing-in-ground-effect vessels for high-speed coastal transit
REGENT designs and manufactures Seaglider craft—electric vessels that operate at airplane speeds over water using wing-in-ground-effect physics. The tech stack reveals a hardware-first, safety-critical engineering organization: C++, RTOS, MATLAB, Simulink, and formal methods (DO-178C, ISO 26262) dominate, paired with simulation (CFD, PyTorch, TensorFlow) and defense-grade requirements management (Jama, Polarion, DOORS). Active hiring is heavily weighted toward engineering (9 of 14 roles, mostly mid-level and manager-track) and focused on production scaling, test campaigns, and DOD contract execution—indicating transition from prototype to certified production.
Notable leadership hires: Chief Engineer
REGENT builds electric Seaglider vessels for regional coastal transportation, targeting routes up to 180 miles at 180 mph with current battery technology. The 12-passenger Viceroy model leverages existing dock infrastructure and reuses standard charging, positioning it as a speed-and-cost alternative to regional air and maritime travel. The company operates under defense contracts (evident from Marine Corps programs in active projects) while pursuing FAA and transport certifications. Headquartered in North Kingstown, Rhode Island, REGENT combines aerospace and marine engineering expertise; the organization is capital- and compliance-intensive, managing simultaneous engineering, testing, and regulatory workstreams.
Core languages: C++, Python, MATLAB. Real-time OS (RTOS), flight control simulators (Simulink), safety standards (DO-178C, ISO 26262). CAD: NX, SolidWorks, Fusion 360. Requirements/traceability: Jama Connect, Polarion, IBM DOORS. ML/vision: PyTorch, TensorFlow, OpenCV.
Envelope expansion and vehicle flight tests, Marine Corps program execution, phased test campaigns, hazard assessments, CFD-to-loads mapping, secure infrastructure deployment, and roadmap toward program-of-record certification.
REGENT's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.