Concept: New York’s tech startup VEUNEX has developed an AI-based health, safety, and environment (HSE) management software that combines AI with surveillance cameras to identify, alert, and track unsafe actions and conditions, and execute the appropriate action plan to improve safety in the workplace. The startup claims that AI-based digital safety solutions can reduce occupational risk factors by 83% and can improve the overall HSE team performance.
Nature of Disruption: The AI-based software prevents incidents through the early detection of anomalies in the industrial context. It leverages CCTV streams to detect anomalies, conduct proper action plans in real-time, and combine that with existing protocols to ensure the highest level of safety by reducing the probability and intensity of risks. The AI engine of the software enables it to check HSE compliance in the camera images. It leverages machine vision technologies, including deep learning to detect and identify human detectable incidents. The platform can identify various HSE concepts and identify unsafe conditions, risk factors, and human errors from live streams. The HSE management software leverages already existing security cameras and helps to avoid the expenditure associated with the installation of additional cameras. VEUNEX claims that the HSE software can be integrated into other systems including fire suppression to create a united safety system besides mitigating damages. The HSE management software offers a visual dashboard that provides access to all detections, alerts, and logs.
Outlook: Human errors can ruin a project, decrease personnel morale, and increase costs associated with project implementation each year. According to the Fatality Assessment and Control Evaluation (FACE) program, it is estimated that between 12 and 13 US workers die each day due to a work-related injury. VEUNEX claims that its AI-based HSE management software combines AI and CCTV streams to detect, alert, and archive unsafe conditions. The startup claims that the HSE software can save about 87% in accident-related compensation, and decrease fatalities by 93%. It is capable of applying multiple deep learning models and other machine vision tools on images received from multiple cameras which minimize the hardware resources.