Automate and improve
your operations

Use operational data, software, and AI to automate processes, support teams,
and optimize performance across machines, assets, and locations.

A new way to run operations

Operations used to depend mainly on people, experience, and manual decisions.

Today, machines, assets, and systems generate large amounts of operational data. This data makes it possible to understand performance, improve processes, and automate operational decisions.

Software and artificial intelligence are becoming part of daily operations. They monitor assets, analyze performance, predict problems, support operators, and automate operational tasks.

This is changing how factories, asset fleets, and operational environments are managed and scaled.

The path to
intelligent operations

Step 1

Connect and collect data

Connect machines, assets, and systems and start collecting operational data such as performance, downtime, usage, quality, and sensor data.

This data forms the foundation for understanding and improving operations.

Step 2

Understand operations

Use dashboards, analytics, and reporting to understand performance, identify bottlenecks, and gain insight into how operations actually run across machines, assets, and locations.

Step 3

Optimize processes

Use operational data to improve planning, maintenance, quality, workflows, and overall operational processes.

Optimization improves efficiency, reliability, and operational performance.

Step 4

Automate and support operations

Use software and AI to automate operational tasks, support operators and engineers, predict issues, and automate operational decisions.

Software and AI become part of daily operations and help run and improve operational processe

Applying AI to operational and IoT data

Artificial intelligence can be applied to many types of operational data, including machine signals, sensors, images, audio, and operational systems. Depending on the use case, AI models can run on edge devices or in the cloud to detect anomalies, predict failures, optimize processes, and support operators and engineers.

Machine and sensor data

Anomaly detection and predictive maintenance

Use AI on machine signals and sensor data to detect abnormal behavior, predict failures, and improve maintenance planning.

Vision and inspection

Quality inspection and visual monitoring

Use computer vision to detect product defects, monitor processes, and improve quality and safety.

Audio and vibration

Audio and vibration pattern analysis

Analyze sound and vibration patterns to detect wear, damage, and incorrect machine behavior at an early stage.

Generative AI

AI assistants for operators and engineers

Use generative AI to analyze manuals, support troubleshooting, and provide operational knowledge and assistance.

Operational data

Performance and process optimization

Use operational and production data to optimize processes, improve performance, and reduce downtime.

AI at the edge
and in the cloud

Artificial intelligence in operations does not run in only one place. Some models run close to machines for real time decisions, while others run in the cloud to analyze data across machines, factories, and locations.

By combining edge AI and cloud AI, operations can make real time decisions locally while continuously improving using cloud data and models.

Edge AI for real time decisions

Edge AI runs directly on devices near machines, sensors, and cameras. This enables real time detection, inspection, and local automation without latency or dependency on a cloud connection.

  • Real time anomaly detection

  • Vision inspection on machines

  • Local alerts and automation

  • Local alerts and automation

  • Works without cloud connection

Cloud AI for insights and optimization

Cloud AI analyzes data across machines, production lines, factories, and asset fleets. This enables prediction, optimization, and continuous improvement across the organization.

  • Predictive maintenance

  • Performance optimization

  • Multi site analytics

  • Model training and updates

  • Long term data analysis

From data
to intelligent operations

Operational data

Collect operational data

Collect data from machines, assets, sensors, and systems and make it available for analytics and AI.

Artificial intelligence

Build and train AI models

Use operational data to build AI models that detect problems, predict failures, and optimize processes.

AI in operations

Use AI in real operations

Run AI models in machines, systems, and workflows to support decisions and automation.

Continuous improvement

Continuously improve operations

Use new data to improve models and continuously improve operations over time.

Built for large scale edge AI deployments

AI creates the most value when it runs close to machines, cameras, and physical assets. Real time detection, visual inspection, anomaly detection, and intelligent control often need to happen directly on devices, not only in the cloud.

Together with our partner NXP, we developed a combined hardware and software solution that brings AI to the edge while staying connected to the cloud. This makes it possible to deploy AI solutions across machines, factories, buildings, vehicles, and other physical environments.

Our joint solution covers the complete pipeline from sensors and gateways to cloud platforms, data engineering, model development, and deployment back to edge devices. Data can be collected from machines and sensors, used to train and improve AI models in the cloud, and then synchronized back to edge devices where models run locally for fast and efficient decision making.

This creates a continuous edge to cloud AI loop where data improves models, models are deployed to devices, and devices generate new data to further improve operations over time.

Scale across

factories, assets, and locations

Machines

Deploy across multiple machines

Deploy the same monitoring, AI, and optimization solutions across multiple machines without rebuilding solutions for each machine.

Production lines

Standardize across production lines

Standardize data models, dashboards, and AI models across production lines while allowing local differences between lines.

Factories

Roll out across multiple factories

Deploy solutions across multiple factories and manage data, performance, and AI models centrally.

Global operations

Manage global asset fleets

Monitor and optimize machines, devices, and assets across multiple locations, countries, and operational environments.

Improve assets and machines over their lifecycle

Connected assets generate data that improves operations today and engineering for tomorrow. This creates continuous lifecycle improvement across machines, assets, and systems.

Connected assets

Assets and machines generate operational data

Connected machines and assets generate data about performance, usage, failures, and operating conditions.

Operational insights

Turn operational data into insights

Analyze operational data to understand performance, failures, usage patterns, and operational behavior.

Engineering improvements

Improve design, software, and maintenance

Use operational insights to improve machine design, components, software, and maintenance strategies.

Next generation assets

Build better next generation assets

Use lifecycle data and insights to develop better machines, devices, and assets for the next generation.

Start your intelligent operations journey?

We help organizations connect assets, use operational data, and deploy AI in real operations. From first pilot to large scale deployment.