The gap between what most industrial operators currently see and what they could see is significant. A plant manager today typically gets production reports from the MES, maintenance alerts from the CMMS when a work order is triggered, and energy data from a building management system that does not talk to the line control system. Three separate systems, three separate views, and no model that connects them.
A digital twin creates that connection. Sensor data from every asset flows into a single platform. The physical layout of the plant, the fleet, or the building is represented visually so operators can navigate to any asset and see its current state. Alert thresholds generate notifications before a problem becomes a failure. Over time, as historical data accumulates, predictive models add a forward-looking layer: not just what the asset is doing now, but what it is likely to do in the next 48 to 96 hours.
The operational impact is specific. Maintenance teams shift from reactive to predictive scheduling. Energy managers can identify which assets are consuming more energy than their operating profile predicts. Operations leaders can make process change decisions with simulation data rather than trial-and-error on the production line.
Capabilities
What we build
IoT sensor data ingestion
Real-time data pipeline from IoT devices and industrial equipment to the digital twin platform. Protocol support: MQTT and MQTT over WebSocket for modern IIoT sensors, OPC-UA for industrial automation systems (PLCs, CNC machines, robots), Modbus TCP and RTU for legacy industrial equipment, and REST API polling for devices with web interfaces. Data ingestion pipeline handles connection management (reconnection on network interruption), message buffering (no data loss during processing backpressure), time-series storage (InfluxDB, TimescaleDB, or AWS Timestream depending on architecture), and data normalization to a consistent schema regardless of device type. Configurable ingestion frequency from sub-second for high-speed process equipment to minute-level for slower-changing assets like HVAC units or structural monitoring sensors. Edge computing layer for assets requiring local pre-processing before cloud transmission -- reduces bandwidth costs and latency for high-frequency sensor streams.
Asset visualization layer
Visual representation of physical assets in the digital twin interface. Options depending on fidelity requirements and budget: Three.js-based web visualization (most flexible, browser-native, lower cost), Unity WebGL for complex 3D asset models requiring physics rendering, or BIM-based visualization for buildings and infrastructure (IFC file integration). 2D schematic views for process flows and pipelines where 3D modeling is not required or cost-effective. Asset hierarchy navigation: plant to zone to equipment line to individual machine to component. Per-asset data overlays showing live sensor readings, current status (running, idle, fault, maintenance), and alert state with color coding. Historical playback mode to review asset behavior over a selected time window -- useful for post-incident analysis and maintenance planning.
Predictive maintenance models
Machine learning models built on historical sensor data to detect anomalies and predict failure events before they occur. Anomaly detection: models trained on normal operating ranges for each asset, with statistical alerts when sensor readings deviate from the expected pattern in ways that correlate with failure events in the training data. Remaining useful life (RUL) estimation: regression models that estimate how many operating hours remain before a component is likely to require replacement, based on degradation patterns observed in historical data. Failure mode classification: supervised models trained on labeled failure events to distinguish which failure mode a current anomaly pattern resembles. Model accuracy depends on the quality and quantity of historical data -- six to twelve months of labeled sensor history with known failure events is the minimum useful training set. We assess your historical data availability during discovery and set realistic accuracy expectations before building predictive models.
Operational dashboard and alert management
Real-time operational dashboard showing fleet-level or plant-level asset status at a glance: active assets, assets in fault state, assets with open alerts, energy consumption versus baseline, and overall equipment effectiveness (OEE) if production data is in scope. Configurable alert rules per asset and per sensor: threshold alerts (value exceeds or drops below a defined level), rate-of-change alerts (value changing faster than the normal operating rate), and pattern-based alerts from predictive models. Alert routing: notifications via email, SMS, PagerDuty, or webhook to your existing maintenance management system. Alert acknowledgment and escalation workflow: alerts that are not acknowledged within a configurable window escalate to a supervisor. Alert history and resolution tracking for maintenance post-mortems. Mobile-responsive dashboard so field technicians can check asset status on a tablet or phone.
ERP, SCADA, and CMMS integration
Integration with enterprise systems to connect the digital twin data layer with operational and business systems. SCADA integration via OPC-UA or OPC-DA bridge -- pulling process data from existing SCADA historian (OSIsoft PI, Wonderware, FactoryTalk) without replacing the SCADA system. ERP integration with SAP, Oracle, or Microsoft Dynamics for production order data, asset master data, and maintenance cost allocation. CMMS integration with IBM Maximo, SAP PM, UpKeep, or Fiix for bi-directional maintenance workflow: predictive maintenance alerts from the digital twin create work orders in the CMMS, and completed work order data flows back to update asset maintenance history in the twin. MES integration for production data (actual output versus planned, quality reject rates, batch data) to add operational context to sensor readings. Integration depth depends on which systems your organization already operates and what data flows are most valuable for the use case.
Simulation and scenario modeling
What-if scenario modeling built on top of the analytical twin data layer. Process change simulation: model the impact of changing a process parameter (temperature, pressure, speed, batch size) before implementing it on the production line. Maintenance schedule optimization: simulate the impact of different preventive maintenance intervals on availability and cost, using the predictive models to inform the trade-off. Energy optimization modeling: identify the operating parameters that minimize energy consumption while staying within production constraints. Capacity planning scenarios: simulate the impact of adding or removing production capacity on throughput and equipment utilization. Simulation capabilities require the analytical twin layer to be in place and operational -- they run against historical data and the predictive models built in the analytical phase. Starting with simulation before the monitoring and analytical layers are established produces unreliable results and is not something we recommend.
What assets are you flying blind on right now?
Book a 30-minute call. Bring your asset list and your current data sources. We will map the gap between what you see today and what a monitoring twin would give you, and tell you what it costs to build.
Digital twin projects encounter predictable problems that experience helps you avoid.
Data quality from existing sensors is rarely clean. Sensors drift. Network interruptions create gaps in time-series data. Different sensors on the same asset report at different frequencies. The data normalization and gap-filling logic that makes the digital twin reliable is often more work than the visualization layer on top of it.
Integration with existing industrial systems is not plug-and-play. SCADA systems, process historians, and CMMS tools vary significantly in their API capabilities. Some expose OPC-UA interfaces. Others require middleware bridges. Some have APIs that are well-documented. Others require vendor consultation to extract data. We do a connectivity assessment during discovery before committing to integration timelines.
Predictive model accuracy depends on historical data quality. You cannot train a useful anomaly detection model on six months of data where three months of readings are missing or where failure events were not labeled. We assess your historical data during discovery. If the data is insufficient for predictive models, we build the monitoring layer first and collect the training data before committing to predictive model timelines.
Organizational adoption is as important as technical accuracy. A digital twin that maintenance teams do not trust or do not use delivers no value. We include alert tuning (reducing false positives until the alert rate is manageable) and user acceptance testing with actual operators in every engagement.
| Scope | Timeline | Cost range |
|---|
| Monitoring twin: sensor ingestion, dashboard, alerts (up to 20 assets) | 10 to 14 weeks | $40,000 to $65,000 |
| Monitoring twin: large-scale (50+ assets, edge computing, SCADA integration) | 14 to 20 weeks | $65,000 to $100,000 |
| Analytical twin: monitoring layer plus predictive maintenance models | 18 to 24 weeks | $80,000 to $140,000 |
| Full analytical twin with ERP and CMMS integration | 20 to 28 weeks | $100,000 to $180,000 |
| Simulation capabilities added to analytical twin | add 12 to 20 weeks | add $40,000 to $80,000 |
All engagements are fixed-price. We scope after reviewing your asset list, existing data sources, and what operational decisions you need the digital twin to support.
We start with the operational question, not the technology. Before deciding on visualization approach, cloud infrastructure, or sensor protocols, we ask: what decisions should your operations team be able to make that they cannot make today? The answer defines the scope. A maintenance team that needs advance warning of HVAC failures needs different capabilities than a production team optimizing line throughput.
Discovery covers your asset inventory, existing sensor coverage, data sources (SCADA, ERP, CMMS, maintenance logs), network connectivity at asset locations, and the alert and reporting workflows your team currently uses. That session produces a scope document, architecture diagram, and fixed-price proposal before we start any development work.
We build incrementally. The monitoring layer ships first and goes to real users before the predictive model work starts. That gives your operations team value earlier and gives us real-world feedback on the alert configuration and dashboard design before we build the more complex analytical layer on top.
We do not hand you a platform and walk away. Digital twin systems require alert tuning as real asset behavior reveals edge cases the initial configuration did not anticipate. We include a tuning period after go-live in every engagement.
To start the conversation, contact us and tell us what assets you need to monitor and what operational problems you are trying to solve. We will scope it from there.
