Quickly diagnosing failures reduces costly defects and downtime. Using breakthrough machine learning approaches, DataProphet DETECT alerts technicians to faults and causes in real-time, enabling corrective action to occur within hours instead of weeks. Using DataProphet DETECT, technicians work more effectively, diagnosing new complex faults while the AI system learns and adapts to real-time data. DataProphet DETECT enables technicians to preempt downtime by performing process changes and maintenance as-and-when faults take place, reducing both machine wear and non-quality.
DataProphet DETECT is powered by both cloud and edge compute capabilities. Using highly optimized edge computing and AI algorithms, real-time data from PLC and SCADA data sources can be used to identify anomalies as and where they happen on the factory floor. This allows DataProphet DETECT to handle larger and more complex modern manufacturing processes without the bottlenecks faced by other systems. From our edge device, flagged anomalies are streamed to the cloud for our AI-driven Diagnostic Algorithm. All data is stored securely in our per-tenant isolated cloud storage solution. This ensures the very best performance, security, and availability for our customers' needs.

Our AI-driven Diagnostics Algorithm streams data to our secure web interface where diagnostics experts and operators can review anomalous behavior in the system to help guide corrective action. Diagnostic Experts can use this interface to provide feedback to our algorithms, by tagging new anomalies for the AI model to learn from and improve through time. Our AI algorithm then learns from this feedback to provide operators with detailed insight on the cause of particular faults, improving the pace of corrective actions from weeks to hours. By providing real-time feedback to operators, process changes become preemptive, correcting process behavior before faults occur. This means a significant improvement in system uptime and measurable cost savings.