Simulation environments built from static engineering data do not reflect the operating state of physical assets. Process models built at commissioning drift from reality as equipment ages and operating conditions change. Testing scenarios run against design assumptions rather than current asset behaviour. Perimattic builds industrial digital twin platforms that synchronise continuously with physical assets via IoT sensor data, maintain models that reflect actual operating conditions, and provide simulation and prediction capabilities that are grounded in real asset behaviour — not engineering assumptions from the commissioning date.
Digital Twin Technology Stack — Python, Node.js, Three.js, AWS IoT, PostgreSQL, InfluxDB, Kafka, Docker, REST APIs, TypeScript, TensorFlow, WebSockets
A digital twin is a living software model of a physical asset, production process, or facility that synchronises continuously with its physical counterpart via IoT sensor data. Unlike a static simulation model built from engineering data at commissioning, a digital twin maintains a model that reflects the actual operating state of the physical asset — incorporating the real degradation, configuration changes, and operating conditions that have accumulated over the asset lifecycle. This synchronisation is what enables digital twins to support use cases that static models cannot: real-time condition monitoring grounded in actual asset state, failure prediction based on actual degradation trajectory rather than design assumptions, and what-if simulation that reflects how the asset actually behaves rather than how it was designed to behave.
The challenge with most industrial digital twin initiatives is that static models drift from physical reality. A simulation model built from design data at commissioning does not account for the wear patterns, calibration drift, and configuration changes that accumulate as equipment operates over years. Predictive models trained on generic equipment data do not capture the specific failure modes and degradation patterns of a particular asset in a particular operating environment. What-if scenarios run against design assumptions can produce recommendations that are physically valid at commissioning but wrong for the asset as it actually exists today. The gap between the model and the physical asset is the gap between a digital twin that drives action and a simulation platform that produces analysis no one trusts enough to act on.
Perimattic builds industrial digital twin platforms starting from the physical asset and its measurable properties — the sensor coverage available, the physics of the process, the failure modes that matter, and the operating envelope the asset actually runs within. Every engagement begins with a thorough mapping of the physical asset before any digital model decisions are made. We design synchronisation architectures that handle the realities of industrial sensor data: noise, gaps, timing irregularities, and protocol diversity. We build predictive models calibrated against real sensor data from the specific asset. And we connect twin insights to the operational systems — MES, ERP, CMMS — where they can drive maintenance decisions, process changes, and production planning.
Asset state accuracy
Engineering model based on commissioning data — does not reflect wear, calibration drift, or configuration changes accumulated since installation
Asset state accuracy
Continuously updated model reflecting actual operating conditions — twin state matches physical asset state at the required synchronisation frequency
Simulation fidelity
Simulations run against design assumptions — outcomes reflect how the asset was designed to behave, not how it actually behaves after years of operation
Simulation fidelity
Simulations grounded in current asset behaviour — models calibrated against real sensor data from the operating asset produce outcomes that reflect physical reality
Failure prediction
Schedule-based maintenance assumptions — maintenance intervals set at commissioning based on design life, not actual degradation trajectory of the specific asset
Failure prediction
Predictions based on actual degradation trajectory from sensor data — remaining useful life estimates reflect the real condition of the specific asset in its operating environment
Process optimisation
Optimisation based on theoretical process parameters — recommendations valid for the asset as designed but potentially wrong for the asset as it currently operates
Process optimisation
Optimisation validated against real operating constraints — what-if scenarios tested against a twin calibrated on real data produce recommendations process engineers trust enough to implement
Change testing
Physical trials required for process changes — new operating parameters must be tested on the physical asset, carrying production risk and requiring planned downtime
Change testing
What-if scenarios tested against digital twin before physical implementation — parameter changes validated against the twin before being applied to the physical process
The operational cost of a digital twin that does not synchronise with the physical asset becomes visible in maintenance interventions that happen too late, process optimisation recommendations that operators override because they do not match actual machine behaviour, and failure prediction alerts that are not trusted because model accuracy has not been validated against real sensor data.
Seven digital twin capability areas covering the complete platform — from asset modelling and IoT integration through simulation, predictive analytics, operator interfaces, and manufacturing system integration.
Custom digital twin platforms connecting physical assets to their software representations via continuous sensor data synchronisation. Built around the specific asset type, physics, available sensor coverage, and use cases — not around a generic simulation framework that requires the problem to fit its architecture.
Engineering model development capturing asset geometry, physics, process behaviour, and operating envelope relevant to the twin's purpose. We assess whether physics-based or data-driven modelling is appropriate for each asset type and use case, and build models that are calibrated against real sensor data from the operating asset.
Sensor data collection, edge processing, and streaming integration that keeps the digital twin synchronised with the physical asset state. We integrate with industrial protocols — OPC-UA, MQTT, Modbus, PROFINET — and cloud IoT platforms to deliver sensor streams to the twin at the update frequency the use case requires.
Simulation environments that allow process engineers to test parameter changes, load scenarios, and failure modes against the digital twin. Because simulations run against a model calibrated on real operating data, scenario outcomes reflect actual asset behaviour rather than design-time assumptions that may no longer be valid.
ML models applied to digital twin state data to predict failure modes, remaining useful life, and optimal operating parameters. Models are trained on actual sensor data from the operating asset rather than generic equipment data — improving sensitivity to the specific failure modes and degradation patterns of that machine.
3D visualisation of asset state, real-time telemetry dashboards, and operator interfaces for twin interaction and simulation control. We build interfaces that give process engineers and operators actionable insight from twin data without requiring them to interact with underlying data models or platform APIs.
Connection of digital twin platforms with MES, ERP, CMMS, and IoT platforms to propagate twin insights into operational workflows. Predicted failures become maintenance work orders. Optimisation recommendations reach production scheduling. Asset health data informs maintenance cost planning — all through structured, maintainable integration interfaces.
A structured six-stage process from free asset discovery through production deployment and ongoing twin model evolution.
We map physical assets, operating parameters, available sensor data, simulation requirements, and integration landscape. This free session establishes a clear picture of the physical asset and its measurable properties before any digital model decisions are made.
We define the model boundary, required data inputs, physics or data-driven modelling approach, synchronisation frequency, and integration requirements — producing a detailed specification before development begins.
We design the platform architecture and build a proof of concept validating real-time synchronisation with the physical asset data at the required update frequency before full development investment begins.
We build the digital twin platform, integrate sensor data streams, develop visualisation and simulation interfaces, and connect the twin to operational systems — delivering working capability at each phase.
We compare twin state against physical measurements, validate simulation scenarios against known physical outcomes, and verify prediction model accuracy before production deployment.
We deploy with monitoring, track model accuracy over time, and update models as asset configuration or operating conditions change — maintaining twin fidelity throughout the asset lifecycle.
We map physical assets, operating parameters, available sensor data, simulation requirements, and integration landscape. This free session establishes a clear picture of the physical asset and its measurable properties before any digital model decisions are made.
We define the model boundary, required data inputs, physics or data-driven modelling approach, synchronisation frequency, and integration requirements — producing a detailed specification before development begins.
We design the platform architecture and build a proof of concept validating real-time synchronisation with the physical asset data at the required update frequency before full development investment begins.
We build the digital twin platform, integrate sensor data streams, develop visualisation and simulation interfaces, and connect the twin to operational systems — delivering working capability at each phase.
We compare twin state against physical measurements, validate simulation scenarios against known physical outcomes, and verify prediction model accuracy before production deployment.
We deploy with monitoring, track model accuracy over time, and update models as asset configuration or operating conditions change — maintaining twin fidelity throughout the asset lifecycle.
Select a domain to see how we design, build, and integrate digital twin platforms for industrial assets and processes.
Pumps, motors, compressors, and other industrial machinery generate rich sensor data that can support a continuously updated digital twin — enabling real-time condition monitoring, failure prediction, and what-if simulation grounded in actual operating behaviour rather than design assumptions.
Pumps, motors, compressors, and other industrial machinery generate rich sensor data that can support a continuously updated digital twin — enabling real-time condition monitoring, failure prediction, and what-if simulation grounded in actual operating behaviour rather than design assumptions.
Production line digital twins capture the state of the full manufacturing process — machine states, throughput rates, quality data, and constraint points — enabling process engineers to optimise scheduling and operating parameters against a model that reflects current production reality.
Building digital twins connected to BIM data and live sensor feeds from building management systems provide facilities managers with a continuously updated model of building energy consumption, occupancy, environmental conditions, and asset state.
Turbines, solar arrays, wind farms, and grid assets can be represented as digital twins that synchronise with SCADA and sensor data — enabling performance optimisation, fault detection, and what-if scenario testing without physical intervention.
Warehouse and logistics network digital twins model inventory positions, flow rates, and constraint points across the supply chain — enabling planners to test routing, inventory positioning, and capacity decisions against a live model before committing to physical changes.
Product digital twins created from engineering models and enriched with in-service sensor data from deployed products enable design teams to validate design changes against real-world operating conditions and accelerate product development cycles.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate digital twin platforms built for real industrial environments from simulation tools that work in a demo but do not reflect actual asset behaviour under production conditions.
Every digital twin engagement begins with a thorough understanding of the physical asset, its failure modes, its operating envelope, and the sensor data available to keep the twin synchronised. We understand the physical before designing the digital. This prevents the most common twin failure mode: a technically functional platform that does not accurately represent the asset it claims to model.
Factory sensor data arrives with noise, gaps, timing irregularities, and protocol idiosyncrasies that generic IoT platforms handle poorly. Our synchronisation architecture handles these conditions without the twin losing accuracy — using edge processing, gap-filling logic, and data quality monitoring to maintain model fidelity even when the sensor environment is imperfect.
What-if simulations that use models calibrated against real sensor data from the operating asset — not engineering assumptions from the commissioning date that may no longer reflect how the asset actually behaves. The result is simulation outcomes that process engineers trust enough to act on, rather than simulation results that require extensive validation before anyone will implement the recommendation.
The team that maps the physical asset and designs the digital model also builds, tests, and deploys the twin platform. There is no handoff between a consulting team and a delivery team, no loss of asset knowledge between discovery and implementation. You work with the same engineering team from the initial asset review through production deployment and model evolution.
“A digital twin that is not continuously synchronised with the physical asset it represents is a simulation model — useful for design, but unable to tell you anything accurate about what the physical asset is doing right now or what it is likely to do next.”
A digital twin in manufacturing is a continuously updated software model of a physical asset, production process, or facility that synchronises with the physical counterpart via sensor data and IoT connectivity. Unlike a static simulation or CAD model built at commissioning, a manufacturing digital twin reflects the current operating state of the physical asset — incorporating the actual degradation, configuration changes, and operating conditions that have accumulated since the asset was installed. This synchronisation enables use cases that static models cannot support: real-time condition monitoring, failure prediction based on actual degradation trajectory, and what-if simulation grounded in current asset behaviour rather than design assumptions.
A simulation model is built from engineering data — design specifications, material properties, and operating assumptions — and runs scenarios against those fixed inputs. It is accurate at commissioning but does not update as the physical asset ages, is modified, or operates under conditions that differ from design assumptions. A digital twin is a live model that continuously synchronises with the physical asset via sensor data. It uses real operating measurements to update the model state, which means simulations run against the twin use current asset behaviour rather than design-time assumptions. The distinction matters most for predictive maintenance and process optimisation: a simulation model tells you what should happen under designed conditions; a digital twin tells you what is likely to happen given the actual current state of the asset.
A digital twin synchronises with the physical asset through sensor data streams delivered via industrial IoT connectivity. Typical data sources include vibration sensors, temperature and pressure transducers, flow meters, power quality monitors, and process variable instruments connected via industrial protocols such as OPC-UA, MQTT, Modbus, or PROFINET. These streams are collected at the edge — often with local processing to filter noise and handle connectivity gaps — and delivered to the twin platform via cloud IoT services such as AWS IoT or Azure IoT Hub. The twin platform ingests these streams into time-series databases, updates the model state at the required synchronisation frequency, and triggers analytics and alerting processes from the updated state.
Yes. Most digital twin development engagements integrate with sensor infrastructure and IoT connectivity that is already installed on the physical asset. The engagement begins with a discovery of the existing sensor coverage, data collection infrastructure, and connectivity protocols in use. We design the twin platform to consume data from existing sources — whether that is an existing SCADA system, a historian database, edge gateways already deployed on the asset, or direct sensor connections. Where sensor coverage gaps exist that would prevent the twin from accurately representing important aspects of asset state, we identify these during discovery and recommend targeted additional instrumentation.
A digital twin enables predictive maintenance by providing a continuously updated model of asset degradation state that goes beyond what threshold-based condition monitoring can detect. The twin aggregates multiple sensor streams — vibration, temperature, current, pressure — and applies machine learning models trained on the asset's historical operating data to detect degradation patterns that precede failure. Because the models are trained on data from the specific asset rather than generic equipment data, they are sensitive to the particular failure modes and degradation trajectories of that machine. The twin also enables remaining useful life estimation — using the current degradation state and operating conditions to project when intervention will be required — allowing maintenance to be scheduled at the optimal time rather than at fixed intervals or after threshold breach.
A digital twin supports process optimisation by providing a simulation environment grounded in actual asset behaviour rather than design assumptions. Process engineers can test changes to operating parameters — temperatures, pressures, speeds, recipe variables — against the digital twin before implementing them physically. Because the twin's model is calibrated against real sensor data from the operating asset, the simulation outcomes reflect the actual response of the physical system rather than the theoretical response predicted by design models. This reduces the risk associated with process changes, eliminates the need for physical trials in many cases, and allows optimisation to be conducted continuously as operating conditions change rather than only at scheduled process review points.
Digital twin platforms can represent individual assets — pumps, motors, compressors, turbines, heat exchangers, CNC machines — as well as larger production systems including production lines, batch processes, continuous processes, and entire facilities. The scope of the twin is defined by the purpose it needs to serve and the sensor data available to keep it synchronised. Individual asset twins are well suited to condition monitoring and predictive maintenance applications. Process twins that represent interconnected assets and their interactions are well suited to throughput optimisation, scheduling, and quality improvement applications. Facility-level twins are used for energy management, space utilisation, and infrastructure maintenance planning.
The accuracy requirement depends on the use case the twin is intended to support. For condition monitoring and anomaly detection, the twin needs to accurately represent the expected operating signature of the asset so that departures from normal behaviour are detectable — this does not require a high-fidelity physics model, but does require good sensor coverage and a well-calibrated baseline. For what-if simulation and process optimisation, the twin needs to accurately represent the relationship between operating parameters and outcomes — which requires model calibration against actual operating data. For remaining useful life prediction, the twin needs to accurately represent the degradation trajectory of the asset — which requires both good sensor coverage and machine learning models trained on sufficient historical data from the asset. We assess accuracy requirements during discovery and design the twin to the level of fidelity needed for the target use cases.
Yes. Integration with MES, ERP, and CMMS platforms is a standard component of industrial digital twin development. We design and build integration layers that allow twin insights — predicted failures, optimisation recommendations, anomaly alerts — to propagate into operational workflows rather than remaining isolated in the twin platform. Typical integrations include creating maintenance work orders in CMMS systems from twin-generated maintenance recommendations, updating production scheduling parameters in MES from twin-derived throughput and availability predictions, and surfacing asset health data in ERP for maintenance cost planning. The integration architecture uses structured APIs and event-driven messaging to ensure twin insights reach the operational systems where they can drive action.
We follow a structured six-stage process: a free asset and process discovery session to map physical assets, available sensor data, simulation requirements, and integration landscape; a digital model specification and data mapping phase that defines model boundaries, required inputs, and synchronisation frequency; a twin architecture and synchronisation proof of concept that validates real-time sync with the physical asset before full development investment; platform development and IoT integration building the full twin platform, sensor data pipelines, and visualisation interfaces; testing, validation, and accuracy verification comparing twin state against physical measurements and validating simulation scenarios; and deployment, monitoring, and twin evolution establishing ongoing model accuracy tracking and update processes as the asset configuration or operating conditions change.
Tell us about your physical assets — the sensor data available, the use cases you need to support, and the operational systems the twin needs to feed. We will show you exactly how a digital twin platform built on real synchronisation architecture can give your engineering and operations teams a live model they can trust and act on.