Artificial intelligence and machine learning
Adopting AI and machine learning often runs into a practical problem: the systems that generate the most valuable data — industrial plants, machinery, legacy infrastructure — don't speak the same language as the tools used to build models. Our experience bridging different technology ecosystems lets us close that gap without requiring a full rewrite of existing systems.
Bringing AI into existing systems, legacy included
Thanks to JCOReflector, a Java application — even on Java 8, with no need to upgrade the JVM or lose existing certifications — can call modern .NET libraries such as ML.NET directly for machine learning inference, with no intermediate REST layer.
Real-time industrial data as a foundation for predictive models
With KNet for OPC and KEFCore we turn field-level OPC-UA data into a real-time, LINQ-queryable Apache Kafka™ stream from .NET applications — a consistent, always-current data foundation for predictive maintenance, anomaly detection, and other machine learning scenarios built on industrial data.
Integration tailored to your constraints
Every context has its own constraints — regulatory certifications, legacy platforms, security requirements. We work with you to understand where AI can bring real value in your specific system, and integrate it without compromising the reliability and compliance you've already built.