IoT Platform use cases
Create edge and cloud IoT platforms
Benefit from a seamless platform that collects data from IoT sensors, processes data in the cloud, leverages ML models and intelligent services that can be deployed on the edge, and monitors all components in real-time. This platform provides a solid foundation for delivering applied IoT and smart manufacturing use cases.
Collect and analyze data from connected devices
Never miss a beat or a data insight by collecting and processing data from multiple consumer and industrial connected devices such as vehicles, wearable devices, consumer electronics, and more.
Reduce the cost of quality control
Optimize quality control with rule-based and AI components for detecting anomalies and defects based on numerical IoT metrics, images, and videos. We help clients create quality control solutions in the most efficient way by combining cloud-native services and custom ML models.
Reduce maintenance costs and risks
Significantly reduce the cost of equipment maintenance, and the risk of failure with AI-powered predictive maintenance methods. Deployed on the IoT platform, the solution includes data collection, modeling, and monitoring components.
Improve production efficiency
Leverage the power of sight and foresight with a combination of computer vision and physical simulations to improve the efficiency of production processes, reduce waste, and improve quality.
Our clients
IoT Platform Starter Kits for AWS and GCP
Our AWS and GCP platform starter kits include data collection and processing components, edge deployment automation, model development tools, and example ML models to accelerate the provision of complete platforms in days, rather than months. The starter kits are engineered for each cloud platform and leverage the cloud-native services in the most optimal ways.
How the IoT Platform works
The platform includes MQTT integrations, CI/CD pipelines for deploying services and models on the edge, and observability components for monitoring.
The collected data are streamed to the cloud where they are pre-processed, validated, labeled, and used for building operational dashboards, reports, and ML models.
We created, and continuously extend, a large library of models for anomaly detection, visual quality control, and predictive maintenance use cases. These models use state-of-the-art ML methods and algorithms developed specifically for IoT scenarios.
Our latest innovations in IoT Analytics
- InsightsIoT Platform: A Starter Kit for AWSIn this article, we describe the IoT Platform Starter Kit for AWS and how it accelerates the provision of an IoT platform in the cloud, integrates with an on-premise facility, and onboards business critical applications like predictive maintenance or visual quality control.Read more
- InsightsBuilding an IoT Platform in GCP: A Starter KitThis blog post describes the IoT Platform Starter Kit for Google Cloud, and how it provides modular components to address various capabilities like data collection, deployment to the edge, IoT devices management, and more, while drastically accelerating time-to-market.Read more
- InsightsAnomaly detection in industrial IoT data using Google Vertex AI: A reference notebookIn this blog post, we focus on IoT data analysis challenges associated with system health monitoring, and how to resolve them with an IoT Platform leveraging Google Vertex AI.Read more
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