Manufacturing

Yes. We have built ML systems for both modern and legacy industrial setups. We can pull data from older PLCs, SCADA systems, and even manual logs, and add a sensor and data layer where one is missing, so you do not need to replace existing machinery to get predictive insight.

We train models on your historical maintenance records, sensor logs such as vibration, temperature, and current draw, and the experience of your maintenance engineers. The model learns the patterns that precede a breakdown and flags them early, so you move from reactive repairs to planned, preventive maintenance.

Yes. Optimization models often lead to meaningful savings in both energy and material usage by tuning process parameters and reducing scrap and rework. The exact savings depend on your lines and current baseline, which we estimate after reviewing your production data.

Vision-based defect detection typically reaches 95 to 99 percent accuracy on well-defined inspection tasks once trained on enough labeled examples of good and defective parts. Accuracy depends on lighting, camera placement, and defect variety, all of which we set up during the build. See how this works in our computer vision development service.

For predictive maintenance, historical maintenance logs and time-series sensor data such as vibration, heat, pressure, or current. For quality inspection, sample images of good and defective parts. If you do not yet capture sensor data, we help specify and integrate the right sensors. Messy or incomplete data is normal, and we handle cleaning and structuring as part of the project.

A focused pilot, for example predictive maintenance on one line or vision inspection at one station, usually runs about 6 to 12 weeks from data review to a working deployment. Plant-wide rollouts take longer and are phased line by line. We confirm the timeline after an initial data and feasibility assessment.

Cost depends on scope: number of lines or machines, whether you need new sensors or cameras, and how deeply it integrates with your MES or ERP. We scope and price after assessing your setup rather than quote a generic range, and we usually start with a contained pilot so you can prove ROI before scaling.

Yes. We can deploy models at the edge, on-premise hardware on the plant floor, for low latency and so production data never leaves your network. Cloud and hybrid options are also available where they make sense for reporting and central dashboards.

Common use cases include predicting bearing or motor wear from vibration and heat data, automated surface defect detection, finding production bottlenecks, and scheduling maintenance during planned windows. Related work spans supply chain optimization and data analytics and business insights for plant-wide visibility.