Technology Differentiation
Physics-informed AI requires physics-grade sensing. Spearix WirelessHART RADiS sensors provide the high-frequency, multi-modal data foundation that enables Choir PINN-GNN models to validate physical constraints in real-time—achieving accuracy impossible with industry-standard 1-minute polling.
The Problem
Pattern-Matching AI
Pattern matching AI is limited due to lack of causal reasoning, context blindness, brittleness and data hungar. It relies on statistical correlation and data pattern reproduction rather than true understanding, reasoning, or context-awareness.
The SENTRIX™ Approach
Physics-Informed AI
SENTRIX™ physics-informed neural networks model how equipment actually degrades: the thermodynamics of battery cells, the vibration signatures of bearing wear, the electrical stress patterns that precede failure utilizing deterministic thresholds and synchronized crosshairs . This isn't black-box ML. It's AI that understands the laws of physics governing your equipment.
The Wireless Sensor Approach
Scalable, Visible, Predictable
Physics-informed AI requires physics-grade sensing. Spearix RADiS multi-core wireless sensors provide the high-frequency, multi-modal data foundation that enables Choir PINN-GNN models to validate physical constraints in real-time—achieving accuracy impossible with industry-standard 1-minute polling.