ABOUT US
Industrial systems are more complex than ever before. A single manufacturing site operates across interconnected production assets, energy infrastructure, material flows and operational constraints. Small disruptions in process stability, equipment performance or demand do not remain isolated. They cascade across throughput, cost, efficiency and emissions.
Engineers and operators spend significant time responding to instability, unplanned downtime, rising energy intensity and avoidable resource loss. A late-stage change in one process step can propagate across the entire operation, reducing performance at scale.
EntroMetrix builds a physics-informed intelligence layer to fundamentally reduce operational complexity, enabling continuous optimisation across production, energy and materials and unlocking a new standard of industrial performance.
OUR APPROACH
Industrial performance emerges from thousands of linked variables across process conditions, equipment response, energy transfer, and material limits. These interactions are not captured by isolated analytics or static rule logic. Effective optimisation depends on a system-level model of how operations behave under real constraints.
Across most sites, decisions are split between planners, engineers, and control layers, each optimising local targets. The key trade-offs between supply chains, throughput, stability, efficiency, and cost are therefore coordinated manually, or handled reactively when performance deviates.
EntroMetrix develops AI that pairs data-driven modelling with physics-informed structure. By encoding thermodynamic relationships, material flow, queueing effects, and operating constraints, the platform aligns optimisation across the system, enabling decisions to be coordinated automatically and improved continuously.
OUR MISSION
Industrial progress now defines economic strength, energy security and climate stability. The systems that manufacture goods, refine materials and power infrastructure determine how efficiently society converts resources into prosperity.
Yet these systems were not designed for the demands they now face. They operate under rising volatility, tighter environmental constraints and increasing performance expectations. Incremental improvement is no longer sufficient. Industry requires structural advances in how complex operations are analysed, coordinated and improved.
AI represents a turning point, but only when grounded in engineering reality. Models must respect physical constraints, operational limits and dynamic interactions across equipment, materials and energy systems. When intelligence is embedded directly into industrial decision-making, optimisation shifts from reactive management to continuous, system-level performance improvement.
EntroMetrix’s mission is to establish a new class of ManufacturingOS that enables industrial organisations to autonomously surface inefficiencies, prioritise high-impact interventions and execute optimisation in real time. By aligning operational profitability with resource productivity and emissions reduction, we aim to accelerate the transition toward more competitive, resilient and sustainable industrial systems.
USE CASE
EntroMetrix is designed to operate across a wide range of industrial environments. We work with both large industrial organisations and smaller family-run manufacturers to improve operational efficiency, reduce energy intensity, strengthen supply chain performance and lower operational emissions.
Our models are already being applied across factories in sectors including IT and data centre infrastructure, chemicals, and textiles and fashion manufacturing. In these environments, EntroMetrix helps operators uncover hidden inefficiencies across production processes, energy systems and material flows, enabling measurable improvements in operational performance, resource productivity and progress toward industrial decarbonisation.
As an illustrative deployment case, textile wet processing highlights where this physics-informed modelling framework can deliver impact. In many developing economies, dyehouses operate with limited system visibility. Processes such as dyeing, washing and finishing rely heavily on steam, heat and water and represent some of the most energy-intensive stages of textile production. Physics-informed modelling can identify operational losses across production systems, resource inputs and planning decisions, improving efficiency while supporting decarbonisation.
JOIN US
EntroMetrix was developed by researchers previously at world-leading research institutions to tackle some of the most critical challenges facing industry. We focus on improving industrial performance at scale, combining rigorous modelling, optimisation, and real-world deployment, delivered at speed.
We are looking for individuals who are comfortable working across disciplines, from process engineering and control systems to machine learning and large-scale software architecture. The problems we tackle require both mathematical precision and practical judgement.
If you are motivated by applying advanced AI to manufacturing, energy and infrastructure, and want to work on systems that directly impact efficiency, profitability and sustainability, we would love to speak to you.
CONTACT
EntroMetrix can deliver up to 25% efficiency improvements across industrial operations.
To discuss your operations with our engineering team or to request a demo, contact info@entrometrix.ai.