Organizations across industries deploy vast networks of sensors—tracking everything from equipment performance and energy usage to traffic flow, supply chains, and safety conditions. The challenge is not collecting this data, but making it useful: sensor streams are often complex, high-volume, and multi-modal (numbers, logs, images, audio, or video), making traditional dashboards slow and limiting for real-time decision-making.

The Sensor Data Intelligence agent makes this data interactive. It ingests structured time-series and tabular measurements alongside unstructured sensor outputs such as images, sounds, and video. Using Corvic’s multi-space analysis engine, it enables users to ask natural-language questions like:

“What patterns link vibration anomalies with temperature spikes in Machine Line 3 over the past month?”

“Show me the correlation between energy consumption and occupancy levels by building zone.”

“Can we predict failure likelihood based on combined acoustic and pressure sensor readings?”

By providing location- and time-aware intelligence, correlations, anomaly detection, and predictive modeling, the agent turns raw sensor data into actionable insights. Enterprises gain faster root-cause detection, proactive maintenance, and better forecasting — without relying on manual data wrangling or static dashboards.

‍