Digital systems often degrade in ways standard monitoring does not reliably capture. A GPU drifting before errors become visible. A network whose structure is weakening long before the event is obvious. A processor accumulating damage that threshold checks fail to separate from normal variation.
The common problem is that conventional monitoring is often too close to the system behaviour it is trying to interpret. When the signal is subtle, thresholding is late. When the dynamics are complex, simple alerts miss progression.
The TCF programme addresses this differently. It uses a proprietary external compute layer designed to learn normal system behaviour and surface meaningful deviation early, without depending on labelled failure examples. The aim is not just to flag anomalies, but to detect transition before conventional monitoring would typically act.
The underlying signal logic has been validated in software across two independent domains. The current research programme is focused on translating that validated software behaviour into low-power hardware suitable for edge, satellite, and body-worn deployment.