Platform

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.

Book a demo Core capabilities

Aerial view of electrical transmission grid

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.

Pipeline and processing facility infrastructure

Core capabilities

Eight capabilities. One platform.

Industrial pipeline infrastructure at dusk

Predictive maintenance & RBI

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

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

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.

High-voltage transmission tower and power lines

Process optimization

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

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.

Oil and gas production facility at twilight

Sustainability analytics

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.

Aerial view of offshore drilling operations

Integrations

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

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 platform.

Capabilities share data and models so you are not stitching vendors after every workshop.

Shared state
Twins feed maintenance and anomaly logic from the same model layer.
Shared models
Optimization and sustainability read the same tags, same physics.
Owned ops
MLOps keeps deployments current without a separate “AI team” project.

Honest comparison

What makes Supi different.

Dimension	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 ignored entirely
Industry coverage	Oil & gas, pharma, chemicals, power, wind	Usually one vertical
Funding model	Bootstrapped, execution-first	VC-funded, growth-at-all-costs

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.

Book a call Read the product page