Who we are and how we work with manufacturing teams across Malaysia.

We train the people who keep automated production lines running.

Production partners

Trusted by robotics teams across Malaysia

Become a partner

Penang Precision Assembly

Deployed our computer vision modules across three SMT lines, cutting manual inspection time by a third.

Selangor Automation Works

Uses our reinforcement learning training to keep six-axis arms stable during high-mix batch changes.

Johor Robotic Systems

Adopted our neural network optimization course for their pick-and-place cells and saw fewer false rejects.

Kuala Lumpur Motion Control

Runs our software coaching program with their maintenance crew to tune servo loops and vision triggers.

Who runs the training floor at Cerebellumcoaching

We are a Kuala Lumpur-based team of machine learning engineers and former production line managers. Our job is to take advanced algorithms out of research papers and put them to work inside automated manufacturing cells, where cycle times and defect rates decide whether a system earns its place on the floor.

01 / Engineering focus

Built by people who have tuned real robotic arms

Every course we run is shaped by hands-on work with PLCs, vision systems, and motion controllers. We do not teach theory in isolation; we show how a model behaves when it has to keep up with a conveyor running at full speed.

02 / Client context

Training matched to your production constraints

Your line has its own cycle times, part geometries, and tolerance limits. We start every engagement by mapping those constraints, then design the curriculum around the specific robots and software stack your team already operates.

03 / Plain language

No jargon walls between engineers and operators

We keep the communication direct. If a concept can be explained with a concrete example from a stamping press or a pick-and-place cell, we use that example instead of a vague metaphor.

04 / Measurable outcomes

Success is a lower reject rate, not a certificate

Our work is judged by what changes on the production floor: fewer false rejects from the vision system, shorter changeover times, and models that keep performing after the consultant leaves the site.

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