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Process KPI Analyzer

The Process KPI Analyzer provides measured, expected, and deviation of plant parameters using user-editable CSVs , Ebsilon Models and Data Science Models. The plant data is imported, validated, and then ingested into the KPI. These KPIs are useful in determining the performance of the plant, and are an integral foundation for use in the associated trending, root cause, and anomaly detection tools.

Degradation Analyzer

The Degradation Analyzers take the overall loss in key plant performance indicators (such as output capacity and energy index) and break them down into the contributing components, such that corrective actions (Capex or Opex) can be taken quantitatively. The key indicators for each type plant will be customized based on customer criticalities. The underlying physics models provide the calculation foundation, and can be tuned by the user to keep pace with regularly scheduled maintenance events.

What-If Analyzer

The What-If Analyzer give the customer direct easy-to-use access to the foundational physics model such that various studies can be made. From a simple browser interface with no training on how to use models, the customer can run the models. With What-If Analyzer, maintenance decisions, operational setpoints decisions, and plant upgrades decision can be backed up with actual physics simulations, taking the guesswork out of the equation.


Industrial data analysis is a resource-intensive process that has a long lifecycle due to inefficiencies in all steps. These limitations cause a longer time between an incident occurring and getting actionable insights, resulting in production losses and increased costs.

Industrial analytics is a layered approach with IIoT analytics that empowers individual departments or teams with deep subject matter expertise to utilise analytics directly, without depending upon a data science or IT team.

Industrial Analytics helps to:


Monitor data in real-time from multiple sources to increase reliability and ensure plant safety


Optimise production and efficiency by timely detection of any deviation of process parameters, validated by historical data


Predict maintenance, maximise asset availability and increase runtime by providing data driven decision support for maintenance support


Self-service analytics provide a robust framework that lets you deploy physics-based and AI/ML models on top of it to not only predict but prescribe for quick actions to save maintenance costs and increase efficiencies in the industrial space.


Deep industrial plant operation experience and world class data sciences expertise

Combining deep domain experience with multi-disciplinary digital expertise, we formulate a unique approach to solve complex industrial problems. Enabling industrial complexes to operate more efficiently and reliably.