Site audit and production line mapping
We start by walking the actual floor with your engineers. We map every robotic arm, conveyor segment, and sensor feed that will feed the training model.
From the first site audit to the final handover, each engagement follows a fixed sequence of checks, builds, and validations. You always know which stage the training is in and what happens next.
A staged rollout across your factory floor, from the first sensor audit to full robotic coordination. Each phase has a clear deliverable and a checkpoint before the next one begins.
We walk the actual assembly line, identify the robots that handle repetitive motion, and map the data sources already available: PLC logs, vision camera feeds, and cycle-time records. The output is a short report on which stations can benefit from machine learning without a full retrofit.
One robotic arm is selected for a focused pilot. We train a vision model to detect misaligned parts and a motion model to adjust grip force. The pilot runs in shadow mode first, so the existing control logic stays untouched while we measure false positives and latency.
Once the pilot passes accuracy thresholds, we switch the station to closed-loop control. The robot now adjusts its own parameters based on the model output. We tune the update frequency, set safety limits, and document the exact conditions under which the model can override the default sequence.
The same model architecture is extended to three or four adjacent stations. We add a simple coordination layer so that a slowdown on one arm triggers a pacing adjustment on the next. This is where the real throughput gains appear, and where we watch for queue buildup.
Your maintenance and process engineers learn to read the model dashboards, interpret confidence scores, and reset the system when a station drifts. We provide a runbook with common failure modes and a direct line to our support team for the first month after go-live.
Every three months we review the model performance against new part types, seasonal production changes, and any mechanical wear. Retraining is scheduled during planned downtime. This is also the point where we decide which additional stations or lines should be brought into the system.
We start by walking the actual floor with your engineers. We map every robotic arm, conveyor segment, and sensor feed that will feed the training model.
Your line generates thousands of data points daily. We help you capture, clean, and label the right subset so the model learns from real conditions, not lab assumptions.
Before anything touches the line, we test candidate algorithms in a simulated environment. This keeps production running while we validate accuracy and latency.
We install the trained model on one robotic station with a human override. For two weeks we compare its decisions against the existing control logic and log every deviation.
Your maintenance and automation staff learn to read model outputs, retrain on new data, and roll back safely. We leave behind runbooks, not just a working model.
Production changes, parts wear, and seasons shift. We schedule regular retraining cycles and review model drift so accuracy stays within your tolerance bands.