ASH sensors have unique capabilities that are not currently available in the market. For example, our sensors use advanced machine learning algorithms to detect patterns or anomalies in data that provide additional value to customers.

Providing an intuitive, easy-to-use interface for customers to interact with the sensors differentiates ASH sensors from competitors. Customers appreciate user-friendly interfaces that require minimal training and provide a better user experience.

Ash Sensors can capture visual data of machine processors, and our sensors can be used to collect other types of data, such as temperature, vibration, or sound by using machine vision techniques to track and identify the specific processor being isolated and monitored.
This enables ASH to collect data on each isolated machine's performance and thus introduces a surrounding noise cancelation technique that is capable of detecting deficiencies early before they cause more serious problems. This can help improve machine performance, reduce downtime, and ultimately save money and improve efficiency in a noisy complex multiple-machine process operating environment.

ASH Sensors can detect small changes in a Structure’s behavior that may indicate early signs of damage or deterioration. This information can be used to identify problems before they become serious and costly to repair. Traditional building sensors, on the other hand, may not be able to detect such changes, as they are designed to monitor environmental conditions rather than structural behavior.

ASH Sensors can be used to verify the accuracy of computer models that simulate a building’s behavior. By comparing the data from ASH sensors to the predictions of the model, engineers can refine their understanding of the building’s behavior and improve the accuracy of their simulations. Traditional building sensors cannot provide this type of information.

ASH Sensors are an exponential force multiplier to traditional sensors (i.e., building automated systems). Clients can now provide precision preventive maintenance activities resulting in unparallel improved maintenance and environmental conditions.