Quality & Yield

Control quality continuously before it becomes a defect.

Agents track material flow and quality metrics in real time, investigating root cause when something moves — before drift becomes a defect.

“We needed a fundamentally different approach — one that could connect operational shop-floor data, add operational context, and turn insights into action and decision-making quickly for our frontline teams and executives.”

Yoshihisa Watanabe  ·  GM of Automotive Division, Digital Transformation Office, Toyota Industries
78%
Defect reduction from predictive quality
60%
Scrap reduction from AI-generated process changes
5%
Yield increase from process standardization
01

Real-time feedback

Agents monitor quality metrics and process parameters together in real time. Drift shows up when it starts, so the agent can begin investigating root cause immediately.

02

Pattern investigation

When a quality metric moves, the agent correlates it against both upstream and downstream parameters, material batch IDs, shift patterns, and machine states. Intermittent defects have causes; the agent finds them by looking across all the data at once.

03

Corrective recommendations

When the agent finds the cause, it recommends a specific corrective action — a process adjustment, parameter change, or material hold — surfaced while there's still time to act.