Many systems observe, compute, report — and forget. After a shift change, a restart, or the next batch, classification often starts from scratch. For real machine understanding, that is a bottleneck: insight without memory stays a snapshot.
Copass takes a different path. In current development, the engine can select completed, reliable insights from the learning process and assign them permanently to each machine. What was learned about this asset yesterday is still there tomorrow — not as generic AI knowledge, but as knowledge of how exactly this machine runs and what Copass has learned about it.
That shifts the perspective: observation becomes machine knowledge. Copass does not only detect change in the moment — it builds on what this machine has already shown under similar conditions. Not just detect. Remember.
For operators and technicians, that means context that carries forward. When a shift reappears, Copass does not classify it in isolation, but in relation to what the engine has already understood and kept for this machine. Machine-specific learning — no plant average, no cloud template.
Copass does not simply forget after the run. Reliable insights stay assigned to the machine and can be drawn on in later runs. Understanding grows over time — not as an opaque score, but as traceable knowledge in machine context.
Context matters: Copass is in development and internal validation — not yet a finished OEM feature, not yet field-tested in a customer plant. But the step is concrete: a machine gets a memory. Not everything is stored — only what is reliable and helps further classification.
For us, this is the next building block of the cognitive engine: local, explainable, per machine. Understanding that stays — and grows with every reliable insight.