AI for Manufacturing

The strongest manufacturing AI projects start with a specific, costly problem rather than a wish to use AI. Typical candidates are an asset whose unplanned stoppages hurt the whole line, a manual inspection step that is slow or inconsistent between shifts, a forecast that keeps leaving you with the wrong stock, or a schedule that planners rebuild by hand every morning.

For each one we ask the same questions: is the data captured today, is there a measurable baseline, and who will act on the output? If sensors, historian tags or inspection images are not being collected yet, the first phase is instrumentation - usually done together with the MES and data work described on our manufacturing software page. Models come once there is something reliable to learn from.

Camera-based inspection works well for visible defects such as scratches, missing components, wrong labels, misaligned parts or contamination - provided lighting, camera position and part presentation are controlled. Most of the effort goes into that physical setup and into collecting a good library of labelled good and defective examples, not into the model itself.

Rare defects are the hard part, because there are few examples to learn from. We often combine a classifier for known defect types with anomaly detection that flags anything that looks unlike a good part, and route uncertain cases to an inspector. Models can run on an edge device at the station so inspection keeps pace with the line and does not depend on an internet connection.

Plants hold years of knowledge in SOPs, equipment manuals, quality procedures and free-text maintenance notes that are hard to search. A retrieval-augmented assistant can answer questions like how to changeover a machine or what was done the last time a fault code appeared, quoting the exact document and revision it used.

We keep these assistants grounded and scoped: they answer from approved documents, show their sources, respect user permissions, and say when they do not know. They support people - they do not change machine settings or sign off quality records. For safety-critical steps the controlled SOP remains the authority.

Shop-floor AI sits where operational technology - PLCs, SCADA, historians and machine networks - meets the business IT network, and that boundary exists for good reasons. We design for it rather than around it: models read data through an industrial gateway or historian interface such as OPC UA or MQTT, the edge device lives in a segmented zone agreed with your OT team, and nothing in the AI stack gets write access to control systems. Outputs go to people, dashboards or the MES, not back to the machine.

Edge hardware also has to be run like plant equipment. That means models packaged so they can be updated remotely and rolled back, devices that keep working and buffer data when the network drops, and monitoring that shows when a camera has moved or a sensor has drifted, because a model is only as good as the signal feeding it.