Research with substance
We ask questions that matter on the shop floor — and actively work on answers.
Our research is not an academic exercise. It flows directly into Copass. Every question ties to real industrial practice.
When should a machine trust its own memory?
Machines can store data — that is no longer the hard part. It gets difficult where stored observations should become experience, and a learning system must decide whether that experience is still valid today.
48 signals validated — next step: 96
In internal testing, Copass successfully completed its expanded signal validation with 48 signals. The next scaling step is already prepared: 96 signals — with the same requirements for context, selectivity, and traceable structure.
Detect change before the alarm fires
Classic monitoring waits for limits. Copass captures how this machine normally runs — and surfaces change before a threshold trips.
Local learning architecture for industrial machines
Copass does not rely on a generic cloud model. We build a local cognitive engine with its own learning architecture — kept small, explainable, and usable in live operation through clear layers.