What is Greenformance?
Greenformance provides a decision layer for industrial systems. It combines machine, energy and process data with engineering process expertise and Industrial AI so that people and AI agents can transparently assess asset states and prepare suitable actions.
Which software products does Greenformance offer?
Greenformance connects three coordinated products:
- Decision Studio for data analysis, modeling and decision preparation
- Runtime for executing validated models and recommendations in live operations
- Physics Core for integrating selected capabilities into existing software and machine solutions
We determine the right product and starting point based on the system, available data and desired decision.
What is Greenformance Decision Studio?
Decision Studio helps technical teams assess existing machine, energy and process data, develop models, run simulations and prepare a transparent basis for decisions by people and AI agents. It can be used in the Greenformance Cloud or on a local server.
What is Greenformance Runtime?
Runtime continuously executes validated models and provides alerts, recommendations or defined optimization steps for live operations. It can be used in the Greenformance Cloud, on local servers and on suitable edge devices.
What is Greenformance Physics Core?
Physics Core is designed for OEMs, system integrators, software providers and internal platform teams. It enables selected Greenformance capabilities to be integrated into existing software, platforms or machine solutions. The specific integration scope is defined together.
How does Greenformance support AI agents?
Greenformance does not simply provide AI agents with isolated raw data. The decision layer brings together engineering meaning, plausibility, uncertainty, recommendation and expected impact. AI agents can therefore support analyses and proposals within defined roles and limits.
Do AI agents make autonomous decisions about machines and assets?
Not automatically. The permitted level of automation depends on the use case, technical safeguards and organizational approvals. Recommendations can first be reviewed by specialists. Only clearly defined and sufficiently safeguarded steps are suitable for automated implementation.
How can I explore the Greenformance products?
You can start with the free CSV data check or the interactive Data Quality Lab. For Decision Studio, we offer personal product demos and use-case evaluations. For Runtime and Physics Core, we jointly determine a suitable pilot or integration scope.
What is the difference between the CSV data check and the Data Quality Lab?
The CSV data check provides an initial automated assessment of a selected machine dataset. The Data Quality Lab interactively demonstrates how different data quality affects analysis and model results. Both are entry points; a technical assessment of the specific industrial use case is performed separately.
Can Greenformance run in the cloud and locally?
Decision Studio can be used in the Greenformance Cloud or on local servers. Suitable edge devices are an additional option for Runtime. The right variant depends on data access, security requirements, latency, computing resources and the existing architecture.
Do we already need a large number of sensors?
Not necessarily. Data is often already available in PLCs, SCADA or historian systems, energy monitoring and other sources. A data and use-case check determines whether it is sufficient for the desired decision and where targeted additions would be useful.
Is Greenformance a dashboard provider?
No. A visualization can be part of a solution, but the core value is the technical basis for decisions behind it. Greenformance not only shows what happened, but also helps explain the meaning of a deviation, possible causes and suitable next steps.
Is Greenformance a DataOps tool?
Greenformance complements DataOps. DataOps improves the availability, structure and accessibility of data. Greenformance focuses on placing that data in its engineering context and making it a trusted basis for decisions by people and AI agents.
Can recommendations be implemented automatically?
Runtime can provide recommendations for review or automatically execute clearly defined optimization steps. Appropriate technical safeguards, validated models, defined limits and organizational approvals are prerequisites.
What is a typical first step?
Possible first steps include the free CSV data check, the Data Quality Lab, a product demo, a brief initial consultation or a data and use-case check. This clarifies the available data, relevant system, desired decision, economic impact and suitable product access.