A machine on the line is running hot. Everyone knows. Nobody has the model.
The engineering team can feel it. What they don't have is the number that says how long before it fails, or the alert that should have reached them two weeks ago.
Sensor data sits in the SCADA system. Equipment history lives in a spreadsheet. Energy data is in a building system that talks to neither. Three views, no connection between them, and a maintenance decision that gets made on a hunch.
A digital twin is the connection: every asset reporting into one live model, thresholds that fire before a fault becomes a failure, and a forward view of what the equipment is about to do.
You stop finding out at the breakdown.
The gap between the data most operators have and the decisions they can make is where the cost lives. Reactive maintenance costs more than predictive maintenance, and the difference compounds every month the two never meet. For manufacturing and logistics operators, that margin is the business case, not the technology itself.
A digital twin pays off when you have assets, data, and a decision you can't make today.
Everything on the left should already be true for your operation. Even one thing on the right, and a monitoring layer or a simpler dashboard is the smarter first step.
A fit01Manufacturing lines, energy infrastructure, or logistics fleets you run without real-time visibility into asset health.
02Existing sensors reporting into a SCADA system or historian, or a clear case for instrumenting the assets that lack them.
03A specific operational decision you can't make today, and budget for a monitoring build from $40,000.
Not a fitAssets with no sensor coverage and no historical data to train a model on.
You want a hardware vendor; we build the software, not the sensors.
A one-off static model or CAD drawing, not a live representation updated with real-world data.
What we build
What a digital twin build covers
01IoT sensor data ingestion
Real-time data pipeline from IoT devices and industrial equipment to the digital twin platform over MQTT, OPC-UA, and Modbus, with protocol support across modern IIoT sensors, industrial automation systems, and legacy equipment. The pipeline handles reconnection, message buffering, and normalization into time-series storage (InfluxDB or TimescaleDB), with configurable ingestion frequency and an edge layer for local pre-processing where bandwidth matters.
02Asset visualization layer
Visual representation of physical assets in the digital twin interface, built in Three.js, Unity WebGL, or BIM and matched to fidelity requirements and budget from 3D models down to 2D schematics where 3D is not cost-effective. Asset hierarchy navigation runs from plant to component, live data overlays show sensor readings, status, and alert state, and historical playback supports post-incident analysis.
03Predictive maintenance models
Machine learning models built on historical sensor data to detect anomalies and predict failures before they occur, with remaining useful life estimation predicting when a component will need replacement. Model accuracy depends on your data: six to twelve months of labeled sensor history is the minimum useful training set, and we set realistic accuracy expectations during discovery.
04Operational dashboard and alert management
Real-time operational dashboard showing fleet or plant-level asset status at a glance: active assets, faults, open alerts, and OEE where production data is in scope. Configurable alert rules per asset route to email, SMS, PagerDuty, or your maintenance system, unacknowledged alerts escalate to a supervisor, and the dashboard is mobile-responsive for field technicians.
05ERP, SCADA, and CMMS integration
Integration that connects the digital twin data layer with your operational and business systems. SCADA data is pulled from existing historians (OSIsoft PI included) without replacing the SCADA system, ERP integration covers the major platforms (SAP, Oracle, Dynamics), and a bi-directional CMMS workflow across Maximo, UpKeep, and Fiix turns predictive alerts into work orders and flows completed work back into the twin's maintenance history.
06Simulation and scenario modeling
What-if scenario modeling built on top of the analytical twin data layer: simulate process parameter changes before touching the production line, optimize maintenance intervals against availability and cost, model energy use, and run capacity planning scenarios. Simulation requires the monitoring and analytical layers in place first, so starting here produces unreliable results.
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.
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.
What assets are you flying blind on right now?
Bring your asset list and your current data sources. We'll map the gap between what you see today and what a monitoring twin would give you, and tell you what it costs to build.
How it works
From scope to shipped
Every digital twin project follows four phases. Scope is locked and price is fixed before development starts.
- Week 1
01Discovery and scope
We map your asset inventory, existing sensor coverage, data sources (SCADA, ERP, CMMS, maintenance logs), and network connectivity at asset locations. You leave week 1 with a written scope document, architecture diagram, and a fixed-price quote. No development starts without your sign-off.
- Weeks 2-3
02Data architecture and design
We design the ingestion pipeline, data normalization schema, alert rule structure, and visualization layout before writing production code. Design decisions made here cost ten times less than the same decisions made in week 8. The spec is locked before the build starts.
- Weeks 4-16
03Build, integrate, and QA
The monitoring layer ships first and goes to real users before the predictive model work starts. Your operations team gets value earlier and gives us real-world feedback on alert configuration and dashboard design. QA runs in parallel with every sprint, not as a phase at the end.
- Weeks 12+
04Go-live and alert tuning
Production deployment with monitoring activated on launch day. Digital twin systems require alert tuning as real asset behavior reveals edge cases the initial configuration did not anticipate. We include a tuning period and 8 weeks of post-launch support in every engagement.
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin, across AI, SaaS, mobile, automation, and enterprise platforms.
Track record
- average client rating across verified reviews
- 4.9/5
- Clutch, verified reviews
- software and AI products shipped since 2015
- 100+
- RaftLabs, founded 2015
GDPR, HIPAA, and SOC 2 requirements are scoped in week 1, not retrofitted before launch. The team that scopes your twin is the team that ships it, from the sensor audit through go-live and alert tuning.
| 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.
A digital twin rarely stands alone. The predictive layer is AI development applied to your sensor data, the work-order automation around alerts is business process automation, and the plant-floor knowledge trapped in SOPs and inspection records feeds generative AI in manufacturing. When the twin needs to live inside a larger operational platform, that is custom software development built to hold up in production, not a demo that buckles under real sensor load.
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.
- Monitoring twin, $40,000-$65,000
- Real-time sensor ingestion, a visual dashboard, configurable alerts, and ERP or CMMS integration across a defined set of assets. Ships in 10 to 14 weeks.
- Analytical twin, $80,000-$180,000
- The monitoring layer plus predictive maintenance models, anomaly detection, remaining useful life estimation, and full ERP and CMMS integration. Ships in 18 to 28 weeks.
What it costs
Digital twin development, starting at $40,000.
A monitoring layer that ships first and goes to real users, then the predictive and simulation layers built on top of the data it collects.
Starts at $40,000Scoped after we review your asset list and data sources. The monitoring layer ships first; simulation is a separate phase we scope once the analytical twin is live, typically 12 to 20 weeks further out.
Most clients start with the monitoring layer, prove the ROI, then commission the predictive and simulation layers once the data is flowing.
No hourly billing
Once we scope the monitoring layer, that price is locked in writing. No hourly billing, and no scope creep absorbed silently into the final invoice.
One team, start to ship
The team that assesses your problem in week 1 is the team that ships it. No bait-and-switch, no offshore handoff after the contract is signed.
