Industry
Manufacturing and industrial automation solutions for operational efficiency
12 solutions
Sensor-based ML systems that predict equipment failures before they occur — using vibration, temperature, and current data to schedule maintenance before breakdown disrupts production.
Computer vision systems deployed on production lines that inspect parts and surfaces for defects at speeds and accuracies that exceed manual inspection — with zero inspection fatigue.
AI systems that model end-to-end supply chain variables — lead times, demand signals, inventory, and logistics costs — to minimise total landed cost and reduce stock-out risk.
Factory connectivity architecture that links machinery, sensors, and production lines via industrial IoT protocols — enabling real-time monitoring, control, and data-driven operations.
AI-assisted planning systems that translate demand forecasts into optimised production schedules — balancing machine capacity, materials, and labour constraints in real time.
SKU-level demand forecasting models that account for seasonality, promotions, and external signals — improving production and procurement planning accuracy across the product range.
Data systems that track products from design through end-of-life — capturing engineering changes, compliance status, supplier qualifications, and field performance in one connected model.
Generative AI tools that help engineers explore design variants, draft specifications, search technical standards, and reduce the time from concept to validated prototype.
Analytics and automation systems focused on improving throughput, reducing waste, and lowering per-unit production costs — measured against OEE and cost-per-unit baselines.
End-to-end engineering of MES, quality management systems, and industrial software that integrates with ERP and shopfloor equipment — built for the reliability demands of live production.
Advisory engagements that help manufacturers develop a phased Industry 4.0 roadmap — from IoT connectivity and data infrastructure through to autonomous decision-making systems.
End-to-end ML model development for manufacturing applications — from raw sensor data and feature engineering through to production deployment on edge or cloud infrastructure.
