IoT and AI services
Know before
the line stops.
Machine, sensor and process data becomes something operations, engineering and finance can act on. We prove it on one asset first, then extend across the line, the plant and the estate.
If the data you already hold would answer the question, we will say so before proposing any hardware.
Sound familiar?
Most plants do not lack data. They lack access to it.
When production data stays inside machines and spreadsheets, cost, quality and delivery problems are explained after the event rather than prevented.
Production data never leaves the machine
It exists, but only on a local screen, so nobody can compare lines or shifts.
Maintenance is still reactive
Equipment fails, the schedule slips, and the cost lands after the event.
The weekly pack is assembled by hand
Hours spent pulling numbers, leaving little time to act on what they say.
Yield and scrap are explained after the fact
Process parameters drift quietly because nothing is watching them in real time.
Control systems and ERP do not speak
So operational reality and the financial picture are argued rather than reconciled.
What we actually build
Five capabilities, on the Microsoft platform you already run.
Each is scoped to a decision somebody owns, because instrumentation without a decision behind it becomes shelfware.
Digital twins
A working map of the plant.
Azure Digital Twins models sites, lines, assets and processes so teams can monitor and simulate in one place rather than reasoning from separate screens.
Why it matters
Engineering, operations and IT discuss the same representation of the factory.
How we help
We scope the twin to the decisions it has to support, because modelling everything is how these programmes stall.
Sensors and devices
Signals you can trust.
Sensor and device programmes for temperature, vibration, pressure, usage and machine performance, with secure ingestion into Azure IoT services.
Why it matters
Condition data arrives continuously instead of being read manually on a round.
How we help
We agree device identity, connectivity and who owns the hardware before anything is fitted.
Data platform
Operational data beside business data.
Production data lands on Azure and Microsoft Fabric alongside ERP, quality and supply chain data, through governed pipelines built to scale.
Why it matters
Downtime, yield and cost can be examined in one place rather than three.
How we help
We reuse the Dynamics 365 and Fabric knowledge we apply on delivery projects every week.
AI for production
Warning before the stoppage.
Models detect anomalies, forecast likely failures and highlight process drift, with outputs explainable enough for an engineer to act on.
Why it matters
Maintenance moves towards condition-based and planned work.
How we help
We test whether your history is complete enough to predict from before proposing a model.
Operational reporting
The metrics people actually use.
Power BI views for plant, line and asset owners covering OEE, downtime, yield, energy and cost per unit.
Why it matters
Supervisors and directors read the same numbers, defined once.
How we help
We define each measure with the people accountable for it, so reporting stops being contested.
The platform underneath is usually Power BI and Microsoft Fabric, with prediction covered under predictive intelligence.
How we deliver it
From first conversation to production insight.
A pragmatic path that starts small, respects the operations calendar and only scales once value is proven.
Why InteliSense
We start on the shop floor, not in the architecture.
Connected data only pays back when a routine changes because of it.
One asset before the estate
We prove the pattern inside a timebox rather than asking for a plant-wide commitment first.
Operations calendar respected
Work is planned around production, shutdowns and shift patterns, not the other way round.
Connected to the business systems
The same team delivers Dynamics 365, Power Platform and Fabric, so integration is not a handover.
Explainable insight
An engineer has to understand why an alert fired, or it will be ignored within a fortnight.
Honest about readiness
If the history will not support a reliable prediction, we would rather fix the data first.
Accountable afterwards
Devices, pipelines and models need ownership after go-live, and we name who holds it.
Where this goes wrong
IoT programmes stall for predictable reasons.
Ownership, alert quality and a route to scale decide the outcome far more often than Azure capability does.
Sensors were fitted before the question was agreed.
Data arrives in volume, nobody owns the decision it was meant to support, and the programme is quietly shelved.
The pilot proved value but had no route to scale.
A proof of concept without a platform and governance model becomes a second system to maintain.
Alerts were too noisy to trust.
Thresholds set from defaults rather than your process train people to dismiss the very warnings you paid for.
Maintenance routines never changed.
Predictive insight only reduces downtime if planning, parts and people move ahead of the failure.
Before you call
The questions operations and IT leaders ask first.
A conversation, not a demo
Start with one asset.
Tell us which machine or line causes the most disruption and what you already measure. We will tell you whether existing data would answer it, and what a proof of concept would involve.