A defect caught at the line costs cents; the same defect caught at the customer costs a recall. AI in manufacturing is a margin question disguised as a vision problem.

Where we work

Discrete manufacturers across automotive, electronics, white goods, and capital equipment. Process industries with continuous-flow plants. Plant operators digitising shop floors that span equipment from three vendor generations and four decades. Engineering teams whose institutional knowledge sits in PDFs, service manuals, and the heads of two senior people.

Where AI changes the economics

• Visual inspection. Line-side cameras with edge inference, defect classifiers trained on your historical reject data, and a feedback loop that improves as production runs. Lower false-reject rates than rule-based vision, lower miss rates than human-only inspection.
• Predictive maintenance. Telemetry-driven anomaly detection on critical equipment, integrated with maintenance scheduling, parts inventory, and technician dispatch.
• Engineering copilots. Service manual, CAD, and as-built knowledge surfaced to engineers and field technicians. Hours of search collapsed into a grounded answer with the citation.
• Generative design assistance. First-pass design exploration constrained by manufacturing, cost, and serviceability requirements — augmenting the design engineer, not replacing them.
• Quality root-cause analysis. Connecting field returns to production batch, supplier lot, and process parameters faster than the current incident workflow allows.

OT-safe deployment

Shop-floor AI lives on the wrong side of the IT/OT boundary if it is not designed for it from the start. We deploy inside ISA-95 zoning, with control-system isolation enforced by network architecture, not policy. Edge inference where latency or air-gap demands it; cloud where the data permits it.

What we will not do

Run AI inside safety-instrumented functions without independent functional-safety review. Replace OT vendors’ control logic with our own. Sell a smart-factory platform that requires you to rip out the systems your operations team is already trained on.

What you get

Production-line and back-office AI integrated with the systems your plant already runs — MES, SCADA, ERP, CMMS, PLM. Measured against OEE, first-pass yield, mean time between failures, and engineering throughput. The plant gets more efficient; the system explains how.