Why we cap our batches at 25–30 students.

There is no elaborate theory behind our batch size. The reasoning is almost disappointingly plain: we want to be able to focus on the students.

That's it. That's the whole argument. But it's worth spelling out what it actually costs to hold that line, and what happens when a program doesn't.

What forty in a room actually means

Picture a trainer standing in front of forty students. On paper it works — one person can address forty people, the material gets covered, the syllabus completes on schedule.

In practice, something quieter goes wrong.

The trainer can no longer see individuals. They can see a room. They know roughly whether the room is following, and they have no idea which specific four people lost the thread twenty minutes ago and have been nodding along ever since.

Those four students won't raise their hands. Asking a question in front of forty people is a social cost most students won't pay, particularly the ones already unsure of themselves — which is exactly the group that most needs to ask.

So they don't ask. They fall a little further behind each session. And the trainer, who would have helped instantly if they'd known, never finds out until an assessment weeks later.

Where the number comes from

We settled on 25 to 30 students per batch — not from a study, but from watching what actually happens at different sizes.

Twenty-five to thirty is where student attention and economic sustainability overlap. Not a compromise between them — the range where both actually hold.

This applies to online batches too

A reasonable assumption is that online classes escape this constraint. The room can't run out of chairs, so why cap it?

Because the limit was never the chairs. The limit is the trainer's attention, and that is identical whether the students are in a room or on a screen.

If anything, online makes it harder. In a physical classroom a trainer reads faces — the pause, the frown, the person who has stopped writing. Online, most of that signal is gone. Holding the same batch size online is what keeps the remaining signal usable: the trainer can still notice who has gone quiet, still take questions properly, still run genuine doubt-clearing rather than a queue.

So our online batches carry the same cap as our offline ones. Same reason, applied to a harder case.

What the cap actually buys

The obvious objection

Larger batches earn more. One trainer, one room, one schedule — every additional student is close to pure margin. The commercial case for sixty is straightforward.

We don't run sixty, and it isn't for lack of demand. It's because the outcome we sell is placement, and placement doesn't survive a batch size where students can quietly disappear.

The number that matters to us is not how many students enrolled. It is how many finished, and how many are working in the industry a year later. Batch size is one of the few levers that moves that number directly.

Twenty-five to thirty students. Online or offline. No exceptions when a batch fills up.

It is not a marketing position and it isn't complicated. It is just the largest number at which a trainer can still see every person in the room — and seeing every person is the entire job.