Manufacturing
Intelligent factories, engineered end-to-end.
We deploy perception, forecasting, and autonomy across the industrial stack — from line-side quality inspection to enterprise-scale supply orchestration. Our manufacturing work sits at the intersection of computer vision, time-series intelligence, and closed-loop control, so every model is designed to survive vibration, dust, shift changes, and the unforgiving economics of downtime.
Visual Quality
Sub-millimeter defect detection on high-speed lines.
Predictive Maintenance
Sensor fusion models forecasting failures weeks ahead.
Autonomous Logistics
Fleet routing across AMRs, conveyors, and gantries.
Digital Twin
Physics-aware simulation of processes and yields.
From sensor to shop floor decision
Manufacturing AI fails when it treats the factory as a clean dataset. Zansoc systems begin with the physical constraints of the plant: camera geometry, lighting drift, line cadence, PLC timing, and the human workflows that keep production moving. We co-design sensing with the process engineers who own yield, then train models on plant-realistic distributions — including rare defect classes that only appear after tool wear or material batch changes.
Closed-loop quality and maintenance
Inspection is only useful if it changes the next unit produced. Our deployments feed detections into SPC systems, rework stations, and maintenance schedules with clear latency budgets. Predictive models fuse vibration, thermal, acoustic, and process logs so maintenance teams receive ranked interventions rather than raw anomaly scores. Over time, the same instrumentation supports digital twins that simulate changeovers and capacity scenarios before they hit the line.
Enterprise orchestration
Beyond a single cell, we connect perception and forecasting into multi-site planning: inventory buffers, supplier risk signals, and energy-aware production windows. The result is not a dashboard of models — it is an operating layer that manufacturing leaders can trust under audit, with lineage from every inference back to the training data and calibration state of the sensor that produced it.