Cloud Machine Learning adds intelligence to your product by learning from historical and real-time data across devices, users, and environments. It enables prediction, anomaly detection, optimization, and pattern discovery that allow you to move from reacting to issues toward anticipating and preventing them.

We build data pipelines, models, and retraining workflows that continuously improve as your product runs in the field. This intelligence is integrated directly into your dashboards, alerts, and workflows so insights lead to action, not just reports. Examples include predicting equipment failures, optimizing energy usage, identifying abnormal behavior, or improving quality and performance over time.

This helps you reduce downtime, improve efficiency, and create differentiated value in your product. Instead of offering just connectivity, you offer intelligence, which is what customers increasingly pay for.

Edge Machine Learning brings intelligence directly onto your devices, allowing them to make decisions locally without waiting for cloud responses. This is essential when connectivity is unreliable, latency matters, or data must stay on the device for privacy or cost reasons.

We train models in the cloud and optimize them to run efficiently on embedded hardware, integrating them into firmware and edge runtimes. This enables real-time detection of faults, anomalies, or events, even when the device is offline, ensuring your system remains responsive and resilient in real-world conditions.

This reduces cloud costs, improves response time, increases reliability, and allows your product to function intelligently even at the edge of the network. It turns your devices into active participants in the system, not just data sources.