Machines that report instead of being read
Connect machines, sensors and PLCs so equipment state, cycle counts and alarms flow into dashboards and alerts automatically — no clipboard rounds.
Learn: how many manual readings disappear each shift?
Services / Data & Cloud
IoT & digital twins.Machines that report. Twins that answer.
We connect machines, sensors and buildings into clean, time-synchronised data, build digital twins that answer real operational questions, and layer prediction and optimisation on top — secured like production systems and handed to a team that can run the fleet.
Trustworthy data first. Twins that answer questions. Predictions with measured value.
The business case
Most IoT projects fail at the boring layer.
Not the dashboard — the signal quality, the missing timestamps, the network that drops for an hour. We start where the failures live: clean data from the device up. Everything above it — twins, analytics, AI — then stands on ground that does not move.
When this service fits
Machines or buildings you cannot see into, manual rounds and clipboard readings, unplanned stoppages you want to move into planned windows, or distributed assets nobody monitors centrally.
When something else fits better
If the data already flows reliably and the gap is decisions, data engineering or AI evaluation fits better. If the machines themselves need replacing, no amount of connectivity fixes that — we will say so.
Where this applies
Different estates. The same discipline.
These are example situations, not pre-packaged promises or claims about past results.
Digital twins people actually open
Build a live model of a line, building or fleet — state, history and predicted behaviour — used for planning and diagnosis, not just a 3-D showpiece.
Learn: does a decision each week cite the twin?
Predict maintenance before the stoppage
Watch vibration, temperature, runtime and quality signals to flag degradation early and schedule service inside planned windows.
Learn: how many unplanned stops move into planned windows?
Remote monitoring for distributed assets
Track pumps, HVAC, generators, vehicles or cold chains across sites with one operational view and local alerting at each site.
Learn: does a site failure surface centrally before a customer calls?
Energy & building operations
Metering, occupancy and HVAC analytics that expose waste and automate setpoint adjustments within comfort and safety limits.
Learn: what does the measured saving per month come to?
AI on the edge, where the data is
Run inspection, anomaly detection and optimisation models on gateways and devices at the source, with cloud training and edge inference.
Learn: does the edge model act within its latency budget at 2 a.m.?
How the data flows
From the machine floor to one validated alert.
Sensors and machines feed an edge gateway that buffers, translates and validates. Readings flow through the network into the twin's state model, where analytics watch for real deviations — and one alert reaches the operator view, meaning something.
What we always include
Data quality checks at ingestion, synchronised time, named asset identity, offline buffering and alerting that reaches a named owner.
What we keep out
Dashboards nobody opens, 3-D showpieces without a state model, alerts tuned until nobody trusts them, and hardware recommendations we do not measure first.
Device & data architecture
Clean data from the device up — the twin is only as good as its source.
IoT projects fail at the unglamorous layer: signal quality, time synchronisation, lossy networks and half-documented protocols. We design that layer first, so every consumer above it inherits trustworthy data.
Protocol & connectivity
Modbus, OPC UA, MQTT, BACnet, LoRaWAN and cellular integrations, with a clear mapping from raw registers to named, unit-tagged signals.
Edge gateways
Local buffering, protocol translation and store-and-forward, so a network outage loses nothing and devices keep operating offline.
Time & identity
Synchronised timestamps and consistent asset identity across sites, so events from different sources line up in one history.
Data quality checks
Range, freshness and completeness validation at ingestion, with bad-data alarms — because a silent sensor is worse than no sensor.
The digital twin
A twin is a question-answerer, not a picture.
We build twins that answer operational questions: what is happening now, what happened before similar states, what happens if we change a setting. The visualisation matters less than the state model behind it.
State models
Named assets, relationships and live state — not just telemetry, but what each asset is doing and what it should be doing.
History & replay
Full signal history with replay, so an incident can be reconstructed minute by minute after the fact.
Simulation & what-if
Scenario models where validated — line changes, setpoint changes, load shifts — with their confidence limits stated honestly.
Views per audience
Operations, engineering and management each get the view that answers their questions, driven by one shared state model.
Analytics & AI on the data
From signal history to decisions that get acted on.
Once clean history exists, AI earns its place: anomaly detection tuned to each asset class, predictive maintenance with lead times, and optimisation that stays inside safety envelopes. Every model ships with its measured performance.
Anomaly detection
Per-asset baselines with alerting on deviation, tuned on your history so alarms mean something and alert fatigue stays low.
Predictive maintenance
Degradation models with estimated lead time, feeding planned maintenance windows rather than calendar guesses.
Optimisation within limits
Energy, scheduling and throughput optimisation bounded by hard safety and comfort constraints the system cannot cross.
Edge inference
Models deployed to gateways for low-latency inspection and control, with cloud retraining and versioned model rollout.
Security & operations
Connected machines are attack surface. We treat them that way.
OT and IoT security is a discipline of its own: segmented networks, least-privilege device identity, signed updates and monitored links. We build it in from the first device, not after an audit.
Network segmentation
IT/OT separation with controlled data paths, so a compromise in the office network cannot reach the plant floor or the devices.
Device identity & access
Per-device credentials, rotated certificates and least-privilege topics, so one compromised device cannot impersonate the fleet.
Signed updates & recovery
Firmware and model updates signed and staged with a documented rollback path per device class.
Monitoring & response
Link health, device availability and security events monitored centrally, with escalation owners named in the runbook.
Reliability & scale
A fleet of 400 devices must behave like one system.
Pilots are easy; scale is where IoT earns its keep. We design for fleet-wide operations: bulk provisioning, staged rollouts, capacity planning and the operational practices that keep hundreds of devices honest.
Provisioning at scale
Automated device enrolment and configuration, so the 300th device ships with the same identity, credentials and config as the first.
Staged rollouts
Updates deployed in waves with health checks between waves, so a bad firmware build touches a handful of devices, not the fleet.
Capacity & cost planning
Message volumes, storage retention and bandwidth sized against growth, with retention policies that keep storage honest.
Field operations
Diagnostics, remote logging and a spare-parts-and-procedures picture, so site visits are planned rather than frantic.
Tools & frameworks
Matched to the machines and buildings you already have.
We work across the protocols, gateways and platforms that industrial and building estates actually run. These are options, not a requirement to use everything on the list.
Protocols & fieldbus
MQTTOPC UAModbusBACnetLoRaWANCAN bus
Protocols matched to the machines and buildings already on site.
Edge & gateways
Edge gatewaysNVIDIA JetsonRaspberry Pi / industrial PCsNode-RED
Local processing, buffering and translation at the source.
Messaging & platforms
EMQX / MosquittoAzure IoT HubAWS IoT CoreKafka
Ingestion that survives outages and scales past the pilot.
Time-series & storage
TimescaleDBInfluxDBClickHousePostgreSQL
Signal history with retention policies sized to real query needs.
Twin & visualisation
Custom dashboardsGrafanaThree.js / WebGL scenesDigital twin state services
State models and views per audience, not a generic 3-D showpiece.
Analytics & AI
Pythonscikit-learnPyTorchONNX runtime at the edgeStatistical baselining
Anomaly detection, prediction and optimisation with measured performance.
Security
X.509 device certificatesNetwork segmentationSigned OTA updatesTLS everywhere
Identity, segmentation and update integrity for the connected fleet.
Operations
Prometheus / GrafanaAlerting with named ownersFleet provisioning toolsRunbooks
Monitoring, rollout discipline and the practices that run at scale.
Protocol, gateway and platform selection depends on your existing machines, sites and team. We recommend after the estate assessment, not before it.
Delivery approach
Pilot one asset group. Then scale on evidence.
You work with engineers inside the delivery process. The pilot connects one asset group end to end; the fleet rollout follows only what the pilot proves.
- 01
Assess the estate
Inventory machines, sensors, networks and data that exist today; agree the operational questions the twin must answer and the priority assets.
Output: Asset inventory, network assessment and a prioritised use-case plan.
- 02
Design the architecture
Design connectivity, edge, storage, twin state model and security zones; review with your operations and IT teams before hardware orders.
Output: Architecture design, signal mapping and security zone plan.
- 03
Build the pilot
Connect the first asset group end to end — device to dashboard — with data quality checks and a working alert path.
Output: Running pilot with validated data, dashboards and alerting.
- 04
Scale & add intelligence
Roll out to the full estate with staged provisioning, then layer anomaly detection, prediction and optimisation on validated history.
Output: Fleet-wide deployment with measured analytics and AI in place.
- 05
Hand over & operate
Transfer runbooks, dashboards and provisioning to your team; establish the model and firmware update cadence your operations run on.
Output: Operated platform your team owns, with documented rollout and update practices.
What your team brings
A decision-maker, site access and the operators who know the machines, your IT and OT security reviewers, and the people who will run the estate afterwards.
Decisions without surprises
The architecture, signal mapping and security zones are agreed in writing before hardware orders. If the assessment says a use case is not viable, that conclusion comes with the evidence.
Handover & ownership
Your estate. Your platform. Knowledge that stays.
Everything lives in your repositories and your infrastructure from day one. Handover includes runbooks, dashboards and the provisioning process, so the fleet keeps improving without the engineers who built it.
Data you can trust at the source
Validated, synchronised, unit-tagged signals from every connected asset — the foundation everything above depends on.
A twin that answers questions
Live state, history and replay per asset, used in real operational decisions rather than admired once.
Predictions with measured value
Maintenance and optimisation models with lead times and savings stated from your own data, not a vendor deck.
A fleet that runs without heroics
Provisioning, staged updates, monitoring and runbooks, so scale does not depend on the engineer who built it.
Delivery does not include unlimited maintenance or a service-level commitment. We can agree ongoing operations support, or hand over fully to your own team.
Related engineering services
Choose the support your next stage needs.
IoT platforms connect to data platforms, private AI and the operations around them.
Questions before starting
The practical details.
Our machines are old. Is this still possible?
Usually yes — most legacy equipment exposes signals through PLCs, Modbus or simple electrical points that gateways can read. The assessment inventories what is readable, what needs a sensor added, and what genuinely cannot be connected, so you know the honest scope before spending.
What is a digital twin, in practical terms?
A live, structured model of your assets: what each one is doing now, its full history, and — where validated — how it responds to changes. It answers operational questions and supports replay after incidents. A 3-D render with no state model behind it is decoration, and we do not build those.
Do we need the 3-D visualisation?
Only if someone will use it. Some operations want a spatial view of a line or building; others work better with tables, trends and alerts. The twin's value is the state model; the views are chosen per audience and we will recommend the simpler one when it is the better tool.
Can AI predict failures reliably?
For assets with sufficient history and clear degradation signals, often yes, with lead times we can measure. For sparse or low-signal assets, honest anomaly detection beats false promises. Every model ships with its measured performance, and we retire the ones that do not earn their keep.
How do you keep the plant network secure?
Segmented networks with controlled data paths, per-device identities and certificates, signed updates, and monitored links. We follow IEC 62443-aligned practices for OT environments and review the design with your IT security team before anything is connected.
What about internet outages at remote sites?
Edge gateways buffer locally and forward when the link returns, so no readings are lost. Devices keep operating on their last configuration, and local alerting works offline. Connectivity is treated as an unreliable resource, not an assumption.
Can this run without sending data to a cloud provider?
Yes. The full stack — ingestion, storage, twin, analytics — can run on your own infrastructure or a private region, with only the data you choose leaving the site. This pairs directly with our self-hosted & private AI service where models must also stay inside the perimeter.
What does handover look like?
Your team owns the code, the infrastructure definitions, the dashboards and the operational runbooks. We train your operators and engineers during delivery, and the update cadence — firmware, models, configuration — is documented so the fleet keeps improving without us.
Start with the estate
Tell us which machines you cannot see into.
We will assess what can be connected — honestly.
Share the estate, the sites and the operational questions you need answered. We will inventory what exists, assess what is readable and propose a pilot that proves it before the fleet spend.