Connect your factory floor with IoT, enable predictive maintenance, and modernize legacy ERP systems for Industry 4.0.
Manufacturing margins are tight, and downtime is expensive. A single unexpected machine failure can cost thousands or millions depending on your operation. But most factories still operate with limited visibility into equipment health and rely on reactive maintenance rather than prediction. Meanwhile, ERP systems that control your supply chain, quality, and financial reporting are often decades old, resistant to change, and disconnected from the factory floor where the real work happens.
Industry 4.0 is about connecting factory floor equipment with intelligent systems that predict failures, optimize production flows, and drive continuous improvement. IoT sensors stream data from machines. Predictive models identify equipment degradation before failure. Digital twins simulate production scenarios. But to get there, you need to bridge legacy equipment, design sensor networks, build data pipelines, and implement systems that manufacturing teams can actually use. We help manufacturers modernize their technology stack to improve uptime, reduce waste, and respond faster to market changes.
Design sensor networks for machine connectivity. Handle edge computing, data ingestion, and real-time analytics from factory equipment.
Build ML models that predict equipment failures before they happen. Implement alerting and maintenance scheduling to minimize downtime.
Optimize production schedules, minimize changeovers, improve utilization, and respond faster to demand changes.
Implement real-time quality monitoring, statistical process control, traceability, and defect tracking to improve product consistency.
Modernize legacy ERP systems, improve data flow between shop floor and back office, and implement cloud-based alternatives.
Create digital replicas of your production processes to simulate changes, test improvements, and predict outcomes before implementation.
Potential productivity increase through Industry 4.0
Average cost of unexpected machine downtime per hour
Of maintenance costs could be prevented with predictive models
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