I like Rayven for its seamless blend of low-code and full-code power. The industrial-grade AI actually works well in a home environment. We were able to build a complete predictive maintenance system for pond and agitators in under six weeks using drag-and-drop nodes for OPC. The UA Datapool and the AI anomaly detection are great, having been trained on eighteen months of historical vibration temperature data, and they automate workflows that create CMP S tickets when anomalies are detected. This setup saved us from a forty-eight hour compliant shutdown by acting four weeks early. The initial setup was very well supported, with a two-day onsite workshop that mapped our data sources, and we built connectors, covering 85% of our equipment. Overall, it's a very powerful tool that has helped us significantly. Review collected by and hosted on G2.com.
The shared number of notes and options can feel overwhelming for the first two to three weeks. Our operations engineers needed the onboarding workshop to get comfortable. After that, it's smooth. Review collected by and hosted on G2.com.
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