Turn Manufacturing Losses into Operational Excellence
Every micro-stop and quality drift is a signal. AI agents learn how your lines behave, predict losses before they reach the floor, name the root cause and hold throughput at its optimum.
One Agentic Twin™ powers end-to-end production intelligence.
Across cement, mining and metals, plastic manufacturing and select discrete operations, manufacturers lose time and money when equipment underperforms, fails unexpectedly, or operates below its potential. A twin per line connects machine health, process stability and product quality.
Equipment and machine reliability
Fixed and mobile equipment degradation detected before it stops the line.
Process upset prediction
Events such as cobble occurrence in a rolling mill predicted ahead of time.
Quality and scrap reduction
Quality predicted in-process so scrap and rework fall.
Energy minimization
Energy per tonne of production driven down at every operating point.
Measured on real units.
Cement Kiln Performance Optimization
At a 2.6 MTPA plant, the 76-metre rotary kiln (6,000 TPD clinker) is the largest energy consumer. Specific heat consumption sat around 820 kcal/kg clinker, and variable fuel quality and feed chemistry drove burn-zone instability, clinker quality swings and stability-driven interruptions that existing DCS/APC control could not adapt to.
A Kiln Performance Twin (hybrid ML plus physics replica of preheater, calciner, kiln and cooler), an AI Optimizer running Bayesian search for minimum energy per ton within quality and stability constraints, and a GenAI Agent that picks safe, effective levers each iteration from live context and plant SOPs. Optimum set points go to operators open-loop, with what-if analysis on 250+ DCS and lab variables.
2.5% lower specific heat consumption against the 820 kcal/kg baseline, 6% higher TSR without impacting kiln stability, 25–35% less clinker quality variability, 25% better Kiln Stability Index, and throughput held at 6,000 TPD or above with 1.5% upside.
Steel Rolling Mill Cobble Prediction
Cobble events affected production, safety, and equipment reliability, while operators typically learned of the issue only after it occurred.
Anomaly detection models provide early warnings, while GenAI-based root-cause analysis and recommendations use historical event logs, corrective actions, and plant documentation.
3% increase in uptime and production, reduced delays and production losses, and safer operations.
Product Scrap Reduction & Quality Enhancement
Quality was confirmed at inspection, after the scrap had already been made, and systemic causes were hard to separate from one-off events.
Quality is predicted continuously from process conditions. When the model forecasts an out-of-spec outcome, the agent identifies the driving parameters and recommends a correction while the product is still in process. The same analysis, run over history, shows which conditions produce scrap systematically.
24% lower scrap rate on monitored lines, 1.8% higher first-pass quality yield, and 31% fewer customer quality complaints.
Start small, prove value, scale on ROI.
Two to six assets
One unit or system, one solution. Implementation fee at cost, so the first step is not a capital decision.
Validation on live data
Measured against success criteria agreed before the pilot starts, on live plant data.
Facility, country or group
Multiple solutions on the same twin. Economies of scale plus an annual SaaS subscription.
Value-based commercials. Customers routinely reach 15x+ ROI at scale, and savings from early use cases fund the next ones.