Industrial & manufacturing
Telemetry that turns maintenance from reactive into predictive
Most plants already have the sensors. What they lack is a single place where thousands of readings become one legible picture of asset health — early enough to act on.
What the work is up against
01
Data silos per plant, per vendor
Fragmented systems across sites make a real-time view of global asset health impossible, so maintenance stays reactive.
02
Downtime is priced in millions, not hours
An unplanned stop on a production line is the single most expensive event in the operation. Detecting drift days earlier changes the economics entirely.
03
Operational technology outlives its software
Machinery on a twenty-year cycle is paired with control software on a five-year one. Integration, not replacement, is the realistic path.
What we build for industrial teams
Telemetry control centres
A single ingestion and monitoring layer aggregating thousands of sensors across sites, so plant data stops living in per-vendor islands.
Real-time digital twins
A live model of physical assets tracking voltage, temperature and efficiency, giving operators a current picture rather than yesterday’s export.
Predictive maintenance models
Detecting the drift that precedes failure, so an intervention can be scheduled into planned downtime instead of forced during production.
Platforms that sit alongside existing OT
Angular and .NET Core systems that integrate with control software already on the floor, rather than requiring it to be replaced.
Multi-site operational dashboards
One view across global plants, so asset utilisation can be compared and the worst-performing line is visible without a reporting cycle.
How we approach it
Integrate, never rip and replace
Machinery runs on a twenty-year cycle and its control software on a five-year one. Any proposal that begins by replacing what is on the floor is a proposal that will not be approved. The ABB platform was built to read from what already existed.
Unify before you model
Predictive maintenance is impossible while data sits in per-plant silos, because there is no baseline to detect drift against. The first phase of that engagement was consolidation; the modelling only became viable once thousands of sensors reported into one place.
Justify in downtime, not in features
Industrial software is bought against the cost of a stopped line. Framing the work as $2M of avoided downtime and a 15% utilisation gain is not marketing language — it is the only unit of measurement the budget holder uses.
Delivered work
ABB — Switzerland
A centralised industrial IoT monitoring platform unifying fragmented plant data into a single real-time digital twin.
- Potential downtime avoided
- $2M+Potential downtime avoided
- Improvement in asset utilisation
- 15%Improvement in asset utilisation
- Sensor telemetry across sites
- Real-timeSensor telemetry across sites
Questions we get from industrial teams
- Do we need to replace our existing control software?
- No, and proposals that begin that way rarely get approved. The integration path reads from what is already on the floor. Machinery runs on a twenty-year cycle and its control systems on a five-year one, so any platform that assumes a clean slate is planning for a situation that does not exist.
- How much sensor data do we need before predictive maintenance works?
- Enough history to establish a baseline per asset class — typically several months of continuous readings. Without that baseline there is nothing to detect drift against. If the data exists but is siloed per plant, consolidation is the first phase and the modelling follows.
- Can this work across plants in different countries?
- That is usually the point. The value appears when asset utilisation can be compared across sites, which is impossible while each plant reports separately. Multi-site aggregation was the core of the ABB engagement.
- How is the investment justified internally?
- Against the cost of a stopped line, which is the only unit the budget holder uses. Avoided downtime and utilisation percentage are the numbers that travel; feature lists are not.