Find the answers.
Make the decisions.
Build the future.
Fewer unplanned stoppages, more throughput — from data you already have.
From sensor to stream
- S0143.2 °C48.1 °C53.1 °C45.1 °C50.0 °C54.9 °C
- S023.5 mm/s4.0 mm/s4.5 mm/s3.7 mm/s4.2 mm/s3.4 mm/s
- S0329.7 A32.4 A28.0 A30.7 A33.4 A29.0 A
- S042 870 rpm3 011 rpm2 781 rpm2 923 rpm3 065 rpm2 835 rpm
- S0538 m³/h43 m³/h34 m³/h39 m³/h31 m³/h36 m³/h
- S0617.2 bar14.8 bar16.2 bar17.7 bar15.3 bar16.8 bar
- S0793 %87 %91 %95 %89 %92 %
- S0818.1 kPa14.4 kPa16.7 kPa13.0 kPa15.2 kPa17.5 kPa
- S09321 K326 K331 K323 K328 K333 K
- S10649 V670 V691 V657 V678 V644 V
- S11166 kW188 kW152 kW174 kW196 kW160 kW
- S1250.4 Hz50.8 Hz50.2 Hz50.5 Hz49.9 Hz50.3 Hz
- S13107.0 °C112.0 °C104.0 °C108.9 °C100.9 °C105.8 °C
- S143.7 mm/s2.9 mm/s3.4 mm/s3.9 mm/s3.1 mm/s3.6 mm/s
- S1531.0 A26.6 A29.3 A24.9 A27.6 A30.3 A
- S162 565 rpm2 707 rpm2 849 rpm2 619 rpm2 761 rpm2 902 rpm
- S1727 m³/h32 m³/h37 m³/h29 m³/h34 m³/h25 m³/h
- S1814.0 bar15.5 bar13.1 bar14.5 bar16.0 bar13.6 bar
- S1986 %89 %83 %87 %91 %85 %
- S2013.2 kPa15.5 kPa11.8 kPa14.0 kPa10.3 kPa12.6 kPa
- S21324 K316 K321 K326 K318 K323 K
We do not build cookie-cutter AI projects.
We connect your plant, across every type and every manufacturer. Controllers, meters and sensors continuously deliver measurements — on large plants, tens of thousands a second. We bring them together, organise them by discipline and turn the data stream into a robust basis for computing and deciding.
Getting the best out of your plant.
On this basis we build the infrastructure that turns telemetry data into uptime and yield: you spot downtime earlier, plan maintenance and get more out of your assets. Other agencies promise a revolution. We deliver solutions that work — and that your team can run on its own afterwards.
Thousands of sensors produce data, but no answers from it.
We make more of your data and build the IT infrastructure that gets the best out of your assets.
Service Offering
4 stepsDiscovery
Analyse first. Then plan.
You want to make more of the data from your plants and do not know where to begin. You are not alone in that: most successful companies stood at exactly this point.
So we do not start with a quote, we start with a thorough survey — our discovery. We look at what data your plants already produce today. We talk to your maintenance staff and shift leads, because they usually know exactly where it is stuck. And we work out which of it actually pays off.
At the end you have one or two prioritised use cases, a business case you can put in front of your management, and a roadmap. That holds even if we conclude that the big move does not pay off for you right now.
Your benefit
After four weeks you know what is in your data, and you have a basis for a decision instead of a gut feeling. A sound basis for investment does not mean committing yourself for long.
Data foundation
The foundation first, then the models.
It is like building a house: before we can run models or optimise plants, there has to be a foundation. We build the data platform everything else stands on, and we integrate your data. For that we connect controllers, sensors and your existing systems, bring the data into one common shape and make sure it can be relied on.
That gives your engineers and maintenance teams clear dashboards and fast answers. Above all it means nobody has to start from zero every time, and where a number came from stays traceable at any point.
Your benefit
Infrastructure that holds up for the second and third use case as well, instead of being rebuilt for each one. Best of all: this data platform belongs to you, not to us.
The plant speaks up before it stops.
Fewer outages with Predictive Maintenance
Shortly before a plant fails, it behaves differently. The bearing temperature rises a little. The vibration shifts. The shape of the current draw changes. Those signals are not missing from your data, they are buried deep inside it. The difficulty is telling them apart from the hundred other deviations that are simply a load change, a batch change or a warm summer.
That is exactly our work: pulling the patterns that genuinely announce a failure out of what your plants produce anyway. Your maintenance people know many of them from experience. We turn that into a system that never gets tired and looks just as closely at three in the morning.
Your benefit
Predictability and reliability in operation: you know when your plants will fail and can plan that maintenance. It means fewer unplanned stoppages, maintenance windows you can plan, and the chance to order spare parts early and at a better price.
AI & machine learning are never an end in themselves here. They are the tool with which we cut outages and raise performance. We solve real problems instead of producing buzzwords.
The last percent is in the data.
More performance
Once the plants run reliably, it comes down to the last percent: throughput, energy consumption, scrap. Those reserves are rarely where you would look for them. They sit in parameters that were set once years ago and never touched again. In ways of running the process that differ from shift to shift. Or in the one machine that holds the whole line up.
We make visible where performance is being lost, how much of it there is and what is causing it. And we tell you exactly what to do about it.
Your benefit
We deliver better key figures in day-to-day operation. Our platform shows you at any moment which plant they came from.
Our solutions do not get stuck in the proof of concept, they create value in running operation.
The evidence
4 sourcesPotential p. a.€2.43m
- Downtime€0.90m
- Throughput€0.72m
- Maintenance€0.50m
- Energy€0.31m
Step 01 · Discovery
Four levers. About €2.4m a year.
Sources
- MaintainX, State of Industrial Maintenance (2024) — about $25,000 per hour of downtime, n = 1,165
- Siemens Senseye, True Cost of Downtime (2024) — 326 h of downtime per year per large plant
- McKinsey (2017) — downtime −30 to −50 %, a consultancy estimate with no disclosed sample
- US DOE FEMP, O&M Best Practices R3.0 (2010) — maintenance −8 to −12 %
- LBNL, Smart Energy Analytics (2019) — energy −4 to −9 %, a buildings study
- Eurostat — industrial electricity, Germany H2 2025, 20–70 GWh band: €0.1595/kWh
0 – 100 % of respondents
- Culture
- Security
- Technology
- Budget
- Data
Step 02 · Data foundation
The bottleneck is not the model. It is the data.
Sources
- Availability+9 %
- Costs−12 %
- Risk−14 %
- Lifespan+20 %
Step 03 · Fewer failures
Fixed maintenance intervals help with only 18 % of assets.
Sources
- ARC Advisory Group (2015) — only 18 % of failures are age-related, 82 % follow a random pattern; fixed-interval maintenance therefore benefits just 18 % of assets (data compiled by NASA and the US Navy)
- Nowlan & Heap, Reliability-Centered Maintenance (1978) — where the failure patterns come from
- PwC / Mainnovation (2018) — realised figures, n = 268, mostly on single assets and in pilot projects
- ABB / Sapio (2024) — €147,000 per hour of downtime, German median, n = 3,215
- Without improvement
- With energy management
Frame €4.0 – 5.0m p. a.
- Without · today€4.79m
- Without · year 1€4.79m
- Without · year 2€4.79m
- Without · year 3€4.79m
- Without · year 4€4.79m
- Without · year 5€4.79m
- Today€4.79m
- With · year 1€4.55m
- With · year 2€4.50m
- With · year 3€4.46m
- With · year 4€4.41m
- With · year 5€4.37m
Step 04 · More performance
Energy: −€239,000 in year one. The gap keeps widening.
Sources