Digital Twins
You have sensors everywhere and data nowhere. Your assets tell you what broke yesterday. You need to know what's breaking next week.
“All the world's a stage, and all the men and women merely players.
You invested in sensors. They tell you what already happened.
IoT promised operational visibility. Instead, you got more data and the same surprises. The failures still happen. You just get alerts faster.
"We have 500km of pipes, 200 pumps, and 50 treatment plants. Something breaks every week. By the time we know about it, customers are already affected."
You're drowning in sensor data but starving for insight. Thousands of data points stream in constantly, but they don't tell you what's about to fail, only what already has. You're always reacting, never anticipating.
What if the next failure is the catastrophic one? The pipe burst that floods a hospital basement. The pump that dies during a heatwave.
"I either schedule maintenance too early (wasting money on parts that still had life) or too late (dealing with emergency failures). There's no middle ground when I'm guessing."
The manufacturer says replace every 5 years. But some assets run fine for 8; others fail at 3. You don't have the data to know which is which. So you either over-maintain or under-maintain. Both cost money.
What if the bearing you just replaced had another 2 years in it? What if the one you left has 2 weeks?
"Last month we shut down Unit 3 for maintenance. Didn't realise it would overload Unit 4 during peak demand. $200k repair bill because nobody modelled the impact."
Every decision affects ten other things you didn't think about. Systems are interconnected in ways nobody fully understands anymore. The people who built it have retired. The documentation is outdated.
What if your next operational decision causes a cascade failure you didn't see coming?
"We have 15 years of sensor data sitting in historians. Petabytes. And absolutely no way to use it. By the time someone analyses something, the situation has changed."
You invested millions in IoT and SCADA. You have more data than ever. But it's trapped in silos, in different formats, with different timestamps. Nobody can see the full picture. The data exists; the insight doesn't.
What if the pattern that predicts failures has been in your data all along, and nobody can find it?
The cost of not knowing what's coming
Reactive operations aren't just stressful. They're expensive. In emergency repairs, in overtime, in customer impact, in reputation.
Despite billions invested in IoT and sensors, 70% of maintenance remains reactive: fixing things after they break.
Unplanned downtime costs manufacturing alone over $50 billion annually. Infrastructure is even higher.
Average asset utilisation across industries. Massive room for optimisation if you can see what's actually happening.
Equipment failures account for 42% of unplanned downtime. Most were predictable with the right data.
The data paradox
You have more sensor data than ever. You can see exactly what's happening right now. But "right now" is too late. By the time the pressure drops, the bearing is already failing. By the time the temperature spikes, the damage is done. The data tells you what happened, not what's about to happen.
A virtual replica that sees the future
A digital twin doesn't just mirror your assets. It understands them. It learns from history. It predicts what's coming. It lets you test decisions before you make them.
Real-time synchronisation
Your digital twin mirrors physical reality continuously. Every sensor reading, every state change, every operational adjustment, reflected instantly.
Not a static model. A living replica that knows what's happening across your entire operation right now.
Predictive intelligence
AI models trained on your data detect anomalies early, predict failures weeks ahead, and identify optimisation opportunities you couldn't see.
Your data, your models. Not generic industry averages. Predictions specific to how your equipment actually operates.
From sensors to foresight
We connect your existing infrastructure, build a digital model, and layer on AI that learns from your specific operating reality.
Connect to physical reality
We integrate your IoT sensors, SCADA systems, historians, and operational data into a unified data layer.
No rip-and-replace. We work with your existing infrastructure, whatever sensors, protocols, and systems you have. Data stays sovereign.
One view of truth across all your assets and data sources.
Build the digital model
We create a virtual replica of your physical assets, processes, and their relationships.
Not just a 3D visualisation. A functional model that knows how Pump A affects Valve B affects Tank C. Physics-informed where needed.
A model that understands how your system actually works.
Sync in real-time
Sensor data flows continuously into your digital twin, keeping it synchronised with physical reality.
Sub-minute latency for operational decisions. Historical playback for analysis. The model is always current.
Your digital twin mirrors what's happening right now.
Apply predictive AI
Machine learning models detect anomalies, predict failures, and identify optimisation opportunities.
Trained on your data, your assets, your operating conditions. Not generic models, specific to how your equipment actually behaves.
Predictions specific to your reality, not industry averages.
Simulate before acting
Test "what-if" scenarios on your digital twin before implementing them in the real world.
"What happens if we reduce flow by 15%?" "What if Unit 3 goes offline during peak?" Run the simulation, see the results, then decide.
Validate decisions before they have real-world consequences.
Close the loop
Insights connect back to operational systems. Alerts, recommendations, and (where appropriate) automated responses.
Human-in-the-loop for critical decisions. Automated for routine optimisations. You decide the level of autonomy.
From insight to action with the control level you're comfortable with.
From reactive to predictive
A digital twin doesn't just visualise your operations. It transforms them. Fewer surprises. Lower costs. Better decisions.
Predictive maintenance catches failures before they happen. Emergency repairs become scheduled maintenance.
A water utility reduced emergency callouts from 12/month to 2/month within 6 months of deploying their digital twin.
Maintain based on actual condition, not schedules. Replace parts when they need it, not when the calendar says so.
Fleet operator extended average component life by 30% by switching from time-based to condition-based maintenance.
Typical prediction window for equipment failures. Time to order parts, schedule crews, and plan around it.
Pump failure predicted 18 days in advance. Replacement scheduled for the following maintenance window. Zero unplanned downtime.
ML models trained on your data achieve high accuracy on the failures that matter most.
After 6 months of learning, models typically achieve 90%+ accuracy on critical failure modes.
Concerns we hear, and how we address them
You'd be surprised what you can predict with the data you already have. We start with existing sensors, then identify gaps that actually matter. Most organisations need fewer additional sensors than they expect. The data is often there, just not being used.
We specialise in brownfield environments. Water treatment plants from the 1970s. SCADA systems running protocols from the 1990s. Mixed environments with 15 different vendors. If it produces data, we can connect it.
Most "predictive maintenance" projects fail because they use generic models, not models trained on your specific equipment and operating conditions. A pump in Auckland operates differently than one in Arizona. Our models learn from your reality.
Good. Blind trust isn't the goal. Every prediction shows its reasoning: which sensors, which patterns, what confidence level. Operators verify and correct. The system gets better. Trust is earned, not assumed.
Related capabilities
Digital twins integrate with these complementary solutions.