AI-Enabled DigiEngineer™ Operations, For Any Asset Type

Industrial operations are complex. Operators have a lot of fragmented equipment from many different original equipment manufacturers. Each generates an immense amount of data, which makes it very difficult to comprehend and manage.


Alarms and analytics are not enough. Data interpretation is time-consuming and requires a subject matter expert. Today's operating systems lead to loss opportunities and unplanned business interruption. These fragmented systems need to "talk" with one another and communicate their cause and effect.


Our premise is that operators should have an easy-to-use, real-time holistic operating system to understand the health risks of their monitored environments for every worker in every department. 


This type of system does not exist until TODAY.


The rapid growth of digitization with artificial intelligence has created opportunities and challenges in the industry like never before. Loss of expertise due to an aging workforce and challenging succession planning for knowledge transfer/skill retention is outpacing capacity.


Our multi-tasking Generative AI-based DigiEngineer™ (Digital Engineer) is the perfect subject matter expert "co-worker" for operators to do more at scale. By automating thousands of decisions based on subject matter expertise and operational data, we empower industrial operations with our DigiEngineer™ operating system to bridge the skills gap. The DigiEngineer™ generates novel content based on real-time asset and equipment systems health. 


Autonomous bilateral closed-loop capability is available.

Real-Time AI-Based Operational Health

Our unique technology was purpose-built to simplify complex industrial environments so that a non-expert can work like one. The platform understands in real-time the health risks of an asset and all of its dependents of any equipment type.


A risk health indicator is provided to be explored by anyone from anywhere. To determine and understand a specific problem from any manufacturer affecting an asset. 

Success Factors

Go beyond outcomes. Scale, ease of use, and value are the elements for success for artificial intelligence-based real-time operating systems. 

  • Scale - Minimal implementation friction and platform support staff to scale per your operational needs
  • Ease of Use - Intuitive-based day-to-day operations that a non-expert can be trained to work like one
  • Value - Cover high-value assets and troublesome commodity-based units for an expeditious ROI

DigiEngineer™ Sample Use Cases

Industries Served