Industrial AI software provider Sorba AI announced a channel partnership with industrial automation firm E Tech Group to deploy predictive maintenance, advanced process control, and closed-loop AI models that integrate into industrial control environments.
The collaboration combines an industrial AI platform from Sorba AI with E Tech Group’s expertise in controls engineering, OT/IT integration, and lifecycle services. The companies aim to help industrial organizations transition from isolated analytics and pilot projects to AI systems that influence operations, improve asset reliability and optimize process performance, they announced in a release.
See also: The AI layer in connected worker solutions: Promise and practicality
E Tech Group will deploy Sorba AI technologies as part of integrated automation and digital transformation initiatives, aiming to align AI models with control strategies, operational constraints and safety requirements.
The technologies that E Tech Group will deploy include:
- DataBridge: Securely connecting PLCs, DCS, SCADA, historians, MES, and enterprise systems to establish a governed, real-time data foundation
- Detect and Predict (Predictive Maintenance Suite): AI models for anomaly detection, asset health monitoring, and early fault detection
- Simulate & Control (Advanced Process Control Suite): Digital twins, multivariable optimization, and AI-driven APC strategies
- VisionAI Module: Computer vision for quality inspection, defect detection, and visual process monitoring
See also: AI forces us to ask: What does ‘smart’ actually mean?
According to the companies, this enables use cases such as:
- Predictive maintenance models that prevent unplanned downtime and extend asset life.
- AI-driven setpoint optimization that adapts to changing process conditions.
- Continuous quality optimization to reduce scrap, rework, and off-spec production.
- Energy, throughput, and yield optimization driven by real-time process intelligence.
Podcast: Scaling AI won't fix a shaky ERP foundation
These capabilities allow engineering and operations teams to design, deploy and scale AI models using their own domain knowledge, without reliance on scarce data science resources, according to the companies.
