Real-time operational insight
Unify fragmented machine data into shared indicators for asset, line and facility performance.
/ CONNECTED INDUSTRIAL INTELLIGENCE
Smart Industrial IoT Solutions
Turn shop-floor data into trusted decisions.
Scalable, end-to-end Industrial IoT architectures spanning sensors, edge computing, secure connectivity, contextualised data and analytics applications.
Connectivity is only the beginning.Value is created in better decisions.
Industrial IoT securely collects and contextualises data from sensors, machines and control systems, then turns it into decisions operations teams can use. The goal is not more data; it is a trusted data backbone that reduces downtime, energy loss, quality variation and maintenance uncertainty.
Condition-based maintenance
Use vibration, temperature, current and runtime data to detect early signs of degradation.
Energy by product and line
Relate consumption to production context to expose losses and measurable improvement opportunities.
See process drift earlier
Connect process parameters with quality outcomes and act before non-conforming output grows.
From shop floor to application,one continuous data chain.
A scalable IIoT solution is more than a cloud platform. Every link—from field device and edge processing to data context and applications—must be engineered as one system.
Field & assets
Sensors, PLCs, drives, meters, robots and legacy machines.
→Connectivity
Industrial protocols, network segmentation and dependable data access.
→Edge
Local acquisition, filtering, buffering and low-latency analytics.
→Data context
Asset models, time series, work orders and product relationships.
→Applications
Dashboards, alerts, analytics, energy, maintenance and enterprise integration.
OPC UAMQTTModbus TCPPROFINETREST APISQLTime Series
Technology tied tomeasurable outcomes.
Every project begins with a clear operational problem and success metric. The architecture makes that use case reliable, repeatable and scalable.
Machine and line monitoring
Automate state, cycle, downtime and output collection to support OEE and loss analysis.
Predictive-maintenance foundation
Combine condition signals with maintenance history to identify pre-failure behaviour.
Energy and resource management
Relate electricity, gas, water and compressed-air use to lines, shifts and products.
Remote asset management
Monitor distributed facilities centrally and establish controlled, auditable interventions.
Process and quality analytics
Compare critical parameters with quality outcomes to reveal patterns behind variation.
MES, ERP and CMMS integration
Connect shop-floor reality with work orders, maintenance workflows and planning.
Start small.Engineer for scale.
We create value through a verifiable pilot, establish standards and expand to other assets under control.
Discovery and use case
Assets, available data, business objective and target KPI are defined together.
Pilot and data validation
Connectivity is deployed in the selected area; data quality and operational value are proven.
Standardisation
Naming, asset models, cybersecurity, device and integration standards are established.
Scale and improve
The solution expands across lines and sites while adoption and performance are continuously improved.
/ SECURE BY DESIGNConnected,
Connected,
under control.
Extracting production data must not create an unmanaged attack surface. We design IIoT around OT cybersecurity principles, least privilege and auditable connectivity.
- Clear asset inventory and documented data flows
- OT/IT segmentation and controlled conduits
- Authentication, role-based access and certificate management
- Encrypted communication and secure remote access
- Logging, time synchronisation and change records
- Edge buffering and data integrity during loss of connectivity
What to know aboutIndustrial IoT.
Are IIoT and Industry 4.0 the same?
IIoT is the technical backbone that turns machines and industrial assets into connected data sources. Industry 4.0 is the broader transformation of automation, organisation and business models built around that capability.
Can legacy machines connect to an IIoT system?
In most cases, yes. Data can be acquired through the existing PLC, protocol gateways, independent sensors or edge devices without replacing the mechanical asset.
Is cloud deployment mandatory?
No. Data may be processed on premises, in a private cloud or through a hybrid architecture. Latency, data sovereignty, cybersecurity and scale determine the right model.
How is IIoT return on investment measured?
Against KPIs defined before the pilot, such as downtime, energy intensity, maintenance cost, scrap or response time. Installing a platform is not a success metric by itself.
Connect the right data.
Improve the right decision.
Let us identify the first IIoT use case that can deliver measurable operational value in your facility.
Build your IIoT roadmap