Predictive maintenance
Rank work by failure risk instead of the calendar. Spend the maintenance budget where the data says it hurts.
Industrial AI & Digital Twins
Predict failures before they cost you millions.
Supi ties into your SCADA, historians, and ERP, then runs twin-backed models on the equipment that actually moves your numbers — so reliability and ops get a short list of what to fix first.
Managing €4B+ in industrial assets
Monitoring €30M in daily production
GDPR-compliant
What we hear on site
You already know the problem.
Stops are expensive. Most stacks still leave people guessing which alarm matters.
Something trips at two in the morning. Production is down before anyone has a coherent story. By the time you know why, the cost is already on the books.
Screens fill with trends nobody owns. Work orders follow a calendar while the risky asset keeps running. The last “AI” initiative ended with a folder of charts and no owner.
The gap isn't data volume. It's a clear read on what will break next — and what to do before it does.
How we work
Built for the control room, not the conference room.
Supi is meant to sit next to your existing stack: connect, model, alert, and hand humans a decision they can defend.
Ingest from SCADA, historians, MQTT, and ERP — without replacing what already runs.
Build twins that respect physics: limits, ramps, and failure modes your engineers recognise.
Surface a short ranked queue: what is drifting, how soon it matters, and suggested next steps.
Ship to production in weeks with MLOps that keeps models current as conditions change.
Product
Three loops your teams already run — wired to the same twin.
Maintenance, reliability, and process engineering see the same underlying state. Less reconciling spreadsheets, more agreeing on the next move.
Anomaly detection
Spot off-nominal vibration, temperature, and pressure while the shift is still on shift — not after the log file lands.
Process optimization
Tune setpoints inside safe envelopes to squeeze yield and energy without fighting compliance.
Outcomes
Figures we can stand behind in a steering meeting.
Ranges depend on asset mix and how mature your historian is — we sanity-check numbers on your data before anyone promises a headline.
25–40%
Typical band we see for maintenance spend and unplanned downtime once models are in production.
30%
Directional improvement in batch quality and variability where pharma and chemical teams measure every run.
Weeks
From signed data access to models your operators can argue with — not a multi-year science project.
Sectors
Where we spend most of our time.
Same core platform; playbooks differ by regulation, asset class, and how conservative your safety case needs to be.
Oil & gas
Pipelines, rotating equipment, and terminals: fatigue, leakage risk, and windows you can plan instead of firefight.
Pharma & chemicals
Batch records, clean utilities, and reaction profiles: catch drift before it becomes a deviation report.
Power & wind
Thermal and renewable fleets: balance production, fatigue, and grid constraints with one model layer.
Field context
The same sites you run — before you drop in your own shots.
We use a small set of stock industrial photography so layout and pacing stay honest until your comms team swaps assets.
FAQ
Questions we get before a PO hits the system.
Security, legacy kit, and whether this is another pilot-shaped object — answered bluntly.
Research
We still argue with academics — on purpose.
Industrial data is messy. Keeping a foot in applied research helps us separate what works in a paper from what survives a night shift.
Leibniz University
IUTA (Institute for Energy and Environmental Technology)
University of Leicester
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.