One stack, from signal to profit.
Production-grade industrial AI, scoped against your own historian. Each layer stands alone, and each one feeds the next: ComGate moves the data, soft sensors turn it into measurements, MPC turns measurements into optimal moves, and RTO sets the most profitable targets plant-wide.
ComGate
Protocol gateway, simulator and test bench for Modbus TCP / UDP / RTU and OPC UA / DA — engineering, polling, traffic inspection and historian in one tool.
Soft sensing & analytics
Virtual sensors, predictive maintenance, anomaly and fault detection, digital twin and real-time optimisation — on the same delivery stack.
Model Predictive Control
Advanced multivariable controllers that use dynamic process models to predict future behaviour and optimise every control move — within your operating constraints.
Real-Time Optimisation
Plant-wide optimisation that computes the most profitable operating targets for your MPC and APC systems — every hour, at today's prices.
Digital Twin
Real-time digital replicas of your process — for what-if simulation, predictive insight and operator training, all on the same validated model.
Predictive Maintenance
AI models that detect equipment degradation from vibration, temperature and process data — before it becomes unplanned downtime.
Anomaly & Fault Detection
Continuous monitoring that catches abnormal behaviour, sensor drift and emerging faults — with root-cause guidance for operators.
Every solution is designed and developed in-house by our own engineering team — full control over quality, security and support.
Design, deployment and maintenance under one roof: models, integration, infrastructure and security working together.
Deployed on private, dedicated infrastructure behind your firewalls, with uptime and performance visibility.
Round-the-clock support, proactive maintenance and monthly analytics and security reporting.
Which solution fits our plant?
Follow the stack: connectivity first (ComGate), then measurement (soft sensing), then control (MPC) and optimisation (RTO). Monitoring solutions — predictive maintenance, anomaly detection, digital twin — slot in wherever reliability hurts most.
What data do we need to start?
Most engagements start with 6–24 months of historian, lab or document data. We audit coverage in the first week and tell you plainly if it is not enough — before you commit.
How long until we see results?
A proof of concept on your historical data typically lands in 4–8 weeks. Production deployment follows once the KPI target is met and your team signs off.
Where does it run, and who sees our data?
On your infrastructure — plant-side servers, your Azure or AWS tenancy, or an air-gapped network. Data never leaves your boundary, and nothing is used to train third-party models.
Who owns the result?
You do. Handover includes source code, documentation, training sessions and a retraining plan.