Collect more high frequency data from more devices
Cost-effectively collect Terabytes of data per day into a centralized cloud data store, providing a single pane of glass for your manufacturing infrastructure. Pre-process data to ensure data quality and consistency across devices.
Quickly integrate legacy plant data
Easily integrate data from legacy systems, even without software engineering skills. Quix provides out of the box connector templates for popular technologies like Siemens PLC, ModBus, OPC-UA or MQTT, that can also be customised for your use case.
Rapidly innovate with governance
Visualise and query data in real time, or run advanced MATLAB or ML models. 'Low DevOps' tooling ensures R&D teams can build fast, while governance features like projects, environments, permissions, auditing, monitoring, lineage and observability ensure IT maintains control over data quality and production apps.
What can you build with Quix?
Model-based product development
Simulate device behaviour to expedite the development of new products and machines via digital twins. Reduce development time and cost of bringing new products to the market.
Process optimization
Identify production inefficiencies and bottlenecks in real-time, and adjust operations for optimal throughput and resource allocation.
Machine monitoring
Monitor distributed manufacturing plants and complex manufacturing processes centrally. Cost-effectively collect and process Terabytes of granular device data to create a single pane of glass for your manufacturing infrastructure.
Anomaly detection
Quickly identify equipment failures to minimise their impact on production output. Generate alerts down to the machine part to ensure they can be actioned effectively.
Predictive maintenance
Use ML and AI to predict equipment faults before they occur, and schedule maintenance proactively. This minimizes unplanned downtime, ensuring continuous production.
Centralize your data for analytics, monitoring and simulation
Manufacturing use cases
Model-based product development
Process optimization
Identify production inefficiencies and bottlenecks in real-time, and adjust operations for optimal throughput and resource allocation.
Machine monitoring
Anomaly detection
Predictive maintenance
Quality control
Use ML and AI to detect defects and maintain high-quality production standards, as well as reducing waste.