Generative AI in Manufacturing

Computer vision systems trained on your specific product types and defect categories -- surface defects, dimensional variances, assembly errors, and packaging failures. Real-time inspection at line speed with pass/fail output and defect classification. Integration with your MES for automated line stop and routing. We build the model, the inference pipeline, the camera integration, and the dashboard your quality team uses to monitor accuracy. The inspection system that catches what manual checks miss at production speed.

AI-powered scheduling that optimises production sequences across machines, shifts, and material availability. Integration with your ERP demand data, MES machine availability, and inventory levels. Scenario modelling for demand changes, machine breakdowns, and supplier delays. The planning intelligence that replaces spreadsheet-based scheduling with optimised sequences -- reducing changeover time, improving OEE, and meeting customer delivery commitments more consistently.

Machine failure prediction from sensor data -- vibration, temperature, pressure, power draw, and cycle time deviation. Models trained on your historical failure and maintenance records. Maintenance recommendations integrated with your CMMS for automated work order creation. The shift from scheduled to condition-based maintenance that reduces unplanned downtime without over-servicing. We've built AI systems that identified equipment degradation patterns weeks before failure.

Demand forecasting models for production planning and procurement, trained on your order history, seasonality, and customer signals. Supplier risk monitoring that flags delivery risk before it becomes a line stoppage. Inventory optimisation that reduces working capital without increasing stockout risk. The supply chain intelligence that gives your planning team forward visibility rather than reaction to what's already happened.

AI systems that generate and maintain technical documentation from your production data -- work instructions updated when processes change, batch records and certificates of analysis generated from production system data, non-conformance reports drafted from inspection findings, and maintenance records populated from technician inputs. The documentation that used to require hours of manual drafting per batch or process change is generated automatically, with technical staff reviewing and approving rather than writing.

Anomaly detection across process parameters -- temperature, pressure, cycle time, material usage, and yield -- that identifies deviations before they cause scrap or quality failures. Real-time monitoring with threshold-based and ML-based detection for both known failure modes and novel anomalies. Alerts to process engineers with the context needed to investigate. The process monitoring that gives your operators early warning rather than discovering the problem in the quality check.