E Tech Group, Sorba AI launch partnership for AI model integration
What you'll learn:
- The partnership combines Sorba AI’s platform with E Tech Group’s automation and integration expertise.
- AI tools will support predictive maintenance, process optimization, computer vision and real-time data integration.
- The companies aim to improve reliability, quality, efficiency and production performance.
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.
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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
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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.
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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.
About the Author
Sarah MattalianSarah Mattalian
Staff Writer
Sarah Mattalian is a Chicago-based journalist writing for Smart Industry and Automation World, two brands of Endeavor Business Media, covering industry trends and manufacturing technology. In 2025, she graduated with a master's degree in journalism from Northwestern University's Medill School of Journalism, specializing in health, environment and science reporting. She does freelance work as well, covering public health and the environment in Chicagoland and in the Midwest. Her work has appeared in Inside Climate News, Inside Washington Publishers, NBC4 in Washington, D.C., The Durango Herald and North Jersey Daily News. She has a translation certificate in Spanish.
