Asset-level twins
Combine sensor readings, operating history and maintenance records to understand a critical machine’s condition and prioritise the next inspection.
Digital twin software
We develop custom digital twin software that connects equipment data to operational models. Help your team investigate downtime, test process changes and plan maintenance. Start with one use case and a pilot you can evaluate.
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Start with the decision you need to improve
An effective digital twin has a clear purpose. Focus on the asset, process or site where better information can change an operational decision.
Production-line twins
Facility-level twins
Ports and heavy equipment
Find the bottleneck. Test the change.
When a line misses its target, the cause may sit in a machine, a buffer or a scheduling rule. We build a connected model together with your engineers, tracing how those dependencies interact through your own operational data. We validate its assumptions with your team and turn what it shows into a change you can test directly on the line.
Experience with industrial data and visual AI
Opsima
Logistics and supply chain management
May 2024 - Ongoing
Opsima is a forward-thinking technology company delivering digital solutions for managing complex physical operations in industries like construction, logistics, and maritime. Their flagship product, Makara, centralises data from equipment, personnel, and workflows to help organisations monitor, coordinate, and optimise field operations in real time. Makara enhances operational visibility, decision-making, and efficiency to support predictive maintenance, safety improvements, and reduced downtime. With tools tailored for managing large fleets and field activities, Makara empowers businesses to modernise operations, prevent failures, and ensure seamless communication between field teams and the office.
Cybord
Quality control automation system
February 2021 - Ongoing
Cybord is dedicated to removing counterfeit and defective components from production lines. Leveraging AI and big data, Cybord's technology identifies counterfeit, defective, and tampered components, including hardware-based cybersecurity threats. Defects are detected during component placement and before reflow, simplifying rework and enhancing product reliability. Integration with manufacturing execution systems ensures that nonconforming materials are quarantined and prevented from being used.
SCADA System
Oil and gas management software
January 2016 - November 2018
The Oil Well SCADA System is a comprehensive solution designed to empower engineers with crucial insights into integrated electric submersible and surface pumping systems. This system plays a pivotal role in optimizing daily well production and maximizing total reservoir recovery. The SCADA system provides a diverse range of products and services tailored to cater to your ESP and HPS requirements. These include application engineering, equipment design, reliable engineering, equipment service and installation, equipment testing and repair, well testing, mobile test trailers, as well as cable spooling and repair services.
Connect what is happening to what could happen
A useful twin combines a model of your operation with the data needed to keep it relevant. Monitoring shows the current state; simulation helps explore the consequences of a proposed change.
Model a proposed change
Compare scenarios such as a different buffer size, maintenance window or production sequence. Make assumptions explicit and test model behaviour against observed conditions before using its outputs to guide a decision.
Keep the model connected
Link assets and their relationships to telemetry and operational records. Set an update frequency that fits the decision, and make missing or stale data visible so users know how current the picture is.
Engineering for real operating conditions
Your operators know the equipment. Your IT team knows the systems. We bring them into the design process so the software reflects both the operational reality and the technical constraints.
A dependable data foundation
We map data sources, asset identifiers and timestamps before building the operational view. Checking gaps and inconsistent records early helps prevent misleading comparisons later.
Insights inside your workflow
We design around the people who will use the twin: what they need to investigate, who takes action and where that action is recorded. Interfaces and integrations follow that workflow.
Security shaped by your environment
Access permissions, network boundaries and data handling are part of the architecture. We agree integration and deployment choices with your IT and operational teams before connecting systems.
Validation you can assess
We agree acceptance criteria with your engineers and compare model outputs with observed behaviour. The pilot makes its assumptions and limitations clear, giving you evidence for the next investment decision.
From a focused pilot to an informed rollout
Each phase answers a practical question and produces something your team can review. Scope the work around one use case, then use the findings to plan a wider rollout.
1. Agree the scope
Define the decision, users and success measure. Review data availability and access constraints. The output is an agreed pilot scope with priorities, assumptions and acceptance criteria.
2. Establish the data flow
Connect the selected sources and map them to assets. Check quality, timing and interruptions. Your team reviews the data flow and any gaps that affect the model.
3. Build and validate
Develop the model and interface for the agreed use case. Test against real operating conditions with your engineers and document where the results can support a decision.
4. Evaluate and plan ahead
Review the pilot against its success criteria with the people using it. Agree what to refine, what to maintain and what is needed before extending to more assets or sites.
1. Agree the scope
Define the decision, users and success measure. Review data availability and access constraints. The output is an agreed pilot scope with priorities, assumptions and acceptance criteria.
2. Establish the data flow
Connect the selected sources and map them to assets. Check quality, timing and interruptions. Your team reviews the data flow and any gaps that affect the model.
3. Build and validate
Develop the model and interface for the agreed use case. Test against real operating conditions with your engineers and document where the results can support a decision.
4. Evaluate and plan ahead
Review the pilot against its success criteria with the people using it. Agree what to refine, what to maintain and what is needed before extending to more assets or sites.
Add the capabilities your use case needs
A twin can bring together several disciplines. We select the integrations, analytics and interfaces around your requirements and build on the systems you already have.
Predictive maintenance
Industrial IoT integration
Visual AI and inspection
Spatial computing
Frequently asked questions
Not necessarily. Existing sensors, equipment controllers, operational databases and IoT platforms may already provide the inputs you need. A detailed 3D interface is useful when spatial context matters, but it is not required for every twin. We assess the available data first and identify any gaps before proposing extra instrumentation or software.
We assess the interfaces, protocols and access rules of your monitoring, production and maintenance systems during discovery. The integration plan covers data ownership, asset mapping, update frequency and network restrictions. Where direct access is limited, we explore approved alternatives with your IT and operational teams and make the constraints part of the scope.
We define acceptance criteria for the intended decision, then compare model outputs with observed conditions and review discrepancies with your engineers. Validation includes the effects of missing data and changes in operating conditions. The model’s assumptions and limits stay explicit, and changes to equipment or processes may require it to be tested again.
The main factors are data readiness, the number of integrations, model complexity and the validation work required. Start with one asset or process and a defined business question. After reviewing your requirements, we can propose a first phase with deliverables, dependencies and an estimate, then use the pilot findings to plan the next investment.
Bring the operational problem you want to solve, the asset or process involved and a brief overview of your current systems. Examples of the decisions your team struggles with are more useful than a finished specification. Share any known constraints or target dates; we can help identify the questions a discovery phase needs to answer.
Let’s scope your digital twin
