One stack for twins, maintenance, and optimization.
Sensor data, physics-backed models, and MLOps in one place — built for plants and grids, not consumer dashboards bolted onto SCADA.
Why Supi
Built for plants, rigs, and grids — not pitch decks.
Generic AI rarely survives your first winter turnaround. Supi is scoped for operational data: historians, tags, and constraints your engineers already respect.
It layers on what you run today, targets weeks-to-first-value, and is monitored so models do not quietly rot after go-live.
Core capabilities
Eight capabilities. One platform.
Predictive maintenance & RBI
What it does
Fuses tags, trips, and history to rank assets by failure likelihood and criticality — not every line on the PM calendar.
Why it matters
Blanket rounds burn hours on low-risk kit while the real degradation sits elsewhere.
The shift
Calendar rounds → risk-ranked worklists and fewer surprise trips.
Physics-based digital twins
What it does
Live twins for lines, machines, and balance-of-plant with stress and wear informed by how you actually run.
Why it matters
Pure black-box models drift when duty cycles change; physics keeps behaviour explainable.
The shift
Static drawings → continuous state your control room can trust.
Real-time anomaly detection
What it does
Streaming checks on vibration, temperature, pressure, and flow — surfaced when the pattern breaks, not in tomorrow’s CSV.
Why it matters
Hours matter in rotating and high-energy plant; early signal buys intervention time.
The shift
Batch thresholds → immediate, contextual alerts.
Process optimization
What it does
Hybrid models suggest setpoints and timing as conditions move, with traceability for QA and audits.
Why it matters
Small yield gains compound across thousands of batches or tonnes.
The shift
Tribal tuning → repeatable, logged adjustments.
MLOps
What it does
Training, deploy, monitor, retrain — so production accuracy is owned, not wished for after the pilot photo.
Why it matters
Most industrial ML dies from drift and neglect, not bad math.
The shift
Hand-rolled scripts → pipelines that stay current.
Sustainability analytics
What it does
Energy, emissions, and waste tracked from the same operational stream; reporting aligned to frameworks you already file under.
Why it matters
Manual quarterly reconciles steal engineering time and invite errors.
The shift
Spreadsheet fire drills → continuous, exportable views.
Integrations
What it does
SCADA, DCS, historians, LIMS, ERP via APIs, OPC-UA, MQTT — cloud or on-prem.
Why it matters
Nobody replaces a fifteen-year SCADA for a slide deck; the value is in the overlay.
The shift
Siloed exports → one operational picture from day one.
Federated learning
What it does
Improve models across sites without centralizing raw process data — each plant keeps its data local.
Why it matters
Regulated and security-sensitive environments still need shared learning.
The shift
“We can’t pool data” → collaborative training with local custody.
Integrated by design
Not eight separate tools. One integrated platform.
Capabilities share data and models so you are not stitching vendors after every workshop. Intelligence flows in once; better decisions show up where ops already works.
Honest Comparison
What Makes Supi Different
| Supi | Typical Industrial AI Vendor | |
|---|---|---|
| Deployment time | Weeks | Months to quarters |
| Post-pilot survival | MLOps keeps models accurate in production | Most projects stall after the pilot |
| Inspection approach | Data-driven risk-based prioritization | Calendar-based or basic thresholds |
| Model foundation | Physics + ML hybrid | Data-only (breaks when conditions change) |
| Multi-site learning | Federated — no data centralization | Requires data pooling or ignores multi-site |
| Industry coverage | Oil & gas, pharma, chemicals, power, wind | Usually one vertical |
| Funding model | Bootstrapped, execution-first | VC-funded, growth-at-all-costs |
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.