Predictive maintenance
Rank work by failure risk, not by the calendar.
Spend the maintenance budget where the data says it actually hurts.
Industrial AI & Digital Twins
The AI control layer for the equipment that runs your P&L.
Supi connects to your SCADA, historians, and ERP, then runs twin-backed models on the assets that actually move your numbers.
Live across upstream, midstream & processing operations.
Time to first model: weeks, not quarters
- €4B+ industrial assets under management
- €30M of daily production monitored
- GDPR compliant · SOC 2 Type II in progress
The Problem
Stops are expensive. Most stacks still leave people guessing which alarm matters.
Something trips at 02:14. Production is down before anyone has a coherent story. By the time you know why, the cost is already on the books.
- Mean time to diagnosis
- 6.4 hrs
- Alarms reviewed per shift
- 1,200+
- Predictive coverage today
- < 18%
The gap isn't data volume — it's a clear read on what will break next.
Twins drift with operating points — fewer false positives.
How we work
Built for the control room, not the conference room.
Connect, model, alert, and hand humans a decision they can defend.
Connect
SCADA, historians, MQTT, ERP — without ripping out what already runs.
Twin
Models that respect physics: limits, ramps, failure modes engineers recognise.
Rank
A short queue of what is drifting, how soon it matters, and the next step.
Operate
Ship in weeks. MLOps keeps the models current as conditions change.
Product
Three loops your teams already run — wired to the same twin.
Anomaly detection
Spot off-nominal vibration, temperature and pressure while the shift is still on shift.
Not after the log file lands the next morning. Live envelopes per asset class.
Process optimization
Tune setpoints inside safe envelopes to squeeze yield and energy.
Stay inside compliance, free of one-off Excel models the engineer who built them already left.
Outcomes
Figures we can stand behind in a steering meeting.
Ranges depend on asset mix and how mature your historian is — we sanity-check on your data before anyone promises a headline.
- 25–40%
- Maintenance spend & unplanned downtime, in band, once models are in production.
- ~30%
- Directional gain in batch quality and variability for pharma and chemicals.
- Weeks
- From signed data access to models your operators can argue with.
Where it lands
Where we spend most of our time.
Straight talk
The four sentences we get before a PO hits the system.
Security, legacy kit, ROI, timing — answered without the brochure voice.
- “Our systems are old.”
- Good. We integrate with SCADA, legacy ERP, MQTT — no rip-and-replace.
- “AI didn’t stick last time.”
- Most pilots die at handoff. Our MLOps keeps models running long after launch.
- “Is it worth the investment?”
- A single unplanned shutdown costs six figures. Most clients see ROI within a quarter.
- “We’re not ready yet.”
- Three breakdowns ago, the last team said the same. A 30-minute demo costs nothing.
Research partners — we still argue with academics, on purpose
- Leibniz University
- IUTA — Institute for Energy & Environmental Technology
- University of Leicester
Next step
Walk through it on your assets — not ours.
Thirty minutes: data you already have, where a twin would sit, and what good looks like in the first ninety days.