Platform
One platform. Every industrial AI capability you actually need.
From predictive maintenance to sustainability reporting, SUPi brings sensor data, digital twins, and machine learning together in a single platform built for heavy industry — not retrofitted from consumer tech.
Why SUPi
Built for plants, rigs, and grids — not pitch decks.
Most industrial AI platforms are either too generic to be useful or too complex to ever leave the pilot phase. SUPi is different. Every capability is designed for one job: turning messy, real-world operational data into decisions that save money, prevent failures, and extend asset life.
It connects to what you already run. It deploys in weeks. And it stays accurate in production — not just during the demo.
Core Capabilities
Eight capabilities. One platform.
Predictive Maintenance with Risk-Based Inspection
What it does
Analyzes sensor data, historical failure patterns, and real-time operating conditions to forecast equipment failures before they happen. The built-in risk-based inspection engine ranks every asset by degradation probability, operational criticality, and incident history — so your team inspects what matters, not everything on a calendar.
Why it matters
Blanket inspection schedules waste time on low-risk equipment while high-risk assets slip through. This flips the model: resources go where the actual risk is, maintenance costs drop, and unplanned shutdowns become rare instead of routine.
The shift
Calendar-based inspections and reactive repairs → Risk-prioritized schedules, 25–40% lower maintenance costs, extended asset lifespan.
Physics-Based Digital Twins
What it does
Creates virtual replicas of physical assets — pipelines, compressors, turbines, reactors — using physics simulations that model real-time stress, fatigue, thermal behavior, and degradation based on actual operating conditions.
Why it matters
Pure data-driven models break when conditions change. Physics-based twins understand why equipment behaves the way it does, making predictions dramatically more accurate in complex environments like offshore platforms, chemical plants, and wind farms.
The shift
Periodic manual simulations and engineering approximations → Continuous, real-time digital replicas driving 30–50% efficiency gains.
Real-Time Anomaly Detection
What it does
Continuously monitors incoming data streams — vibration, temperature, pressure, flow — and flags deviations the moment they appear. Not in tomorrow's batch report. Now.
Why it matters
In petrochemical plants and power stations, a missed anomaly can escalate from a minor issue to a safety incident in hours. Early detection gives your team time to respond before a deviation becomes a shutdown — or worse.
The shift
Delayed batch alerts and manual threshold checks → Instant, AI-driven notifications that catch problems while they're still small.
Process Optimization
What it does
Uses hybrid physics + ML models to analyze production workflows and recommend real-time parameter adjustments — temperatures, pressures, flow rates, timing — tailored to your specific plant dynamics.
Why it matters
Small inefficiencies compound fast in continuous manufacturing. A 2% yield improvement across thousands of batches isn't marginal — it's material. And every recommendation is traceable for compliance.
The shift
Manual tuning based on operator experience and gut feel → Data-driven optimization delivering 30% improvement in quality and throughput.
MLOps Pipelines
What it does
Automates the full lifecycle of your ML models — training, deployment, monitoring, and retraining — so predictions stay accurate in production, not just during the initial pilot.
Why it matters
This is where most industrial AI projects die. The pilot works, everyone celebrates, and six months later the model has drifted into irrelevance because nobody maintained it. Our MLOps pipelines prevent that by design.
The shift
Manual model management, outdated predictions, and abandoned pilots → Automated monitoring, continuous retraining, 2x faster deployments that actually stick.
Sustainability Analytics
What it does
Monitors emissions, energy consumption, and waste across your operations in real time. Generates compliance reports aligned with EU Green Deal, ISO 14001, and other regulatory frameworks — automatically.
Why it matters
Sustainability reporting is no longer optional, and doing it manually is a time sink that pulls engineers away from actual operations. Automated tracking means accurate numbers without the quarterly scramble.
The shift
Manual tracking in spreadsheets, error-prone reporting, last-minute audit prep → Automated environmental monitoring and audit-ready documentation.
API & Legacy System Integrations
What it does
Connects to your existing infrastructure — SCADA, DCS, ERP, historians, LIMS — via standard APIs, OPC-UA, and MQTT protocols. Works with AWS, Azure, IBM Watson, and on-premise setups.
Why it matters
Nobody is ripping out a SCADA system that's been running for 15 years. We don't ask you to. SUPi layers on top of what you already have, unifying siloed data into a single operational picture without replacing anything.
The shift
Fragmented data across incompatible systems, manual exports, and siloed dashboards → Unified real-time data flows and comprehensive insights from day one.
Federated Learning
What it does
Trains AI models across multiple distributed sites — different plants, platforms, or regions — without moving sensitive operational data to a central location. Each site contributes to improving the model while keeping its data local.
Why it matters
In regulated industries like pharma, or security-sensitive operations like offshore oil, centralizing data isn't just risky — it's often not allowed. Federated learning lets you benefit from multi-site intelligence while staying fully GDPR-compliant.
The shift
Data privacy constraints limiting AI adoption and scale → Secure, collaborative model training across sites without data exposure.
Integrated by Design
Not eight separate tools. One integrated platform.
These capabilities don't live in silos. The result: a single platform where data flows in, intelligence comes out, and your team makes better decisions — faster and with less effort than stitching together five different vendors.
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 |
See what predictive actually looks like.
Book a 30-minute walkthrough tailored to your operations. We'll show you exactly which assets are at risk and what the ROI looks like — no slides, no fluff.