Factories invest massively in advanced sensors and AI, but the value of this information remains untapped because current AI systems treat data modalities in isolation.

This fragmented approach results in missed insights: critical signals are lost in translation, opportunities for optimization go unseen, and cross-modal anomalies escape detection.

Engineers find themselves toggling between dashboards and manually synthesizing information, which is slow, error-prone, and limits the transformative potential of AI for industry.

Current solutions focus on single-modality analytics, standalone dashboards, or basic data lakes with limited cross-modal AI integration.

They are slow to deploy, require extensive manual engineering, and do not provide seamless actionable intelligence across data types.