Safety and collision-avoidance systems for underground mining and industrial sites
Matrix builds hardware and software for underground mining and industrial environments where workers operate near mobile equipment. The tech stack reveals a hardware-forward company (Altium, VHDL, Verilog, embedded microcontrollers) paired with modern ML infrastructure (TensorFlow, PyTorch) and cloud services (AWS, Azure, GCP) — suggesting computer vision and sensor fusion at the core. Engineering dominance in hiring (11 of 16 active roles) combined with active pain points around test automation and CI/CD indicates the company is scaling production workflows and product reliability as it expands.
Matrix Design Group manufactures and deploys safety and productivity technology for industrial and mining operations. The product suite centers on collision avoidance (OmniPro camera systems), personnel and equipment tracking, atmospheric and methane monitoring, and underground mine networking infrastructure. Founded in 2006 and headquartered in Newburgh, Indiana, the company serves mid-market and enterprise industrial operators where worker proximity to mobile equipment creates hazard zones. Active projects span product development (forklift integration, methane monitoring devices), cloud migration to Azure, test automation, and legacy system support.
Matrix uses embedded systems tools (Altium Designer, VHDL, Verilog, Atmel, AVR microcontrollers), cloud platforms (AWS, Azure, GCP), ML frameworks (TensorFlow, PyTorch), and backend languages (Python, C#, C++, Java, Rust). Frontend is React + TypeScript. Data: PostgreSQL, SQL Server, Oracle.
Current projects include collision-avoidance and tracking system development, methane and atmospheric monitoring products, forklift integration design, Azure cloud migration, test automation infrastructure, CI/CD pipeline implementation, and commissioning of industrial safety systems in underground mines.
Other companies in the same industry, closest in size
Matrix Design Group LLC'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.