MarutAI | The Science of Autonomy

The challenge

Throughput decisions are governed by fixed setpoints and manual overrides. Operators cannot continuously adjust across dozens of interdependent process variables in real time.

The approach

A process twin models line dynamics, equipment interactions, and constraint boundaries from production telemetry. Decision models constructed via VATS optimize output within stated equipment and quality limits.

Expected outcome

Use existing equipment more effectively by adjusting operating decisions within encoded quality and equipment limits.

The challenge

Defect detection depends on statistical sampling and manual inspection. By the time a defect is caught, the root cause may have affected an entire production run.

The approach

Twin-calibrated models identify quality deviations from process signals before they manifest in finished product. Detection operates continuously, not at sampling intervals.

Expected outcome

Detect quality deviations earlier and trace them to contributing conditions in the modeled production process.

The challenge

Demand variability and lead-time uncertainty create inventory buildup, scheduling conflicts, and missed delivery windows.

The approach

Process twins model supplier dynamics and production capacity alongside demand signals. Decision models optimize scheduling and inventory positioning under stated lead-time and demand assumptions.

Expected outcome

Adjust inventory and production schedules as demand and lead-time assumptions change.