From 0 to 1, in epochs.

Why is our estimator a letter E?

Machine learning is a different kind of engineering. There is no blueprint where you list the parts and add up the hours. You train the system in passes over your data, and each pass is called an epoch. Early epochs give a rough shape that handles the easy cases. More epochs sharpen the edges. The gains shrink every week and perfect never arrives, so the real question is how sharp your system needs to be for the job. Our logo is the same idea drawn live: a formula redraws the letter E a little sharper with every epoch. We price projects the same way. You choose the sharpness, and the weeks and the range follow.

1 epoch = 1 week = 1 harmonic. Every week of work trains one more Fourier harmonic onto the mark, and it mirrors one week of work on your model. The first 4 weeks are infrastructure: data audit, eval harness, baseline. The curve has not become an E yet because the model has not become a model yet. You can still prove an eval before you have a model, and we always do.

The Gibbs ripple never reaches zero. The overshoot at each corner stays near 9% of the jump and only narrows toward the corner. Model loss behaves the same way: asymptotic, never quite zero. The ripple at the corners is the analogue of edge-case failures in production. They shrink and tuck inside the confidence band. Clean means the overshoot is smaller than half the stroke, which already happens at k≈16, the MVP. The long tail out to 120 weeks pays, week by week, for what the regulator, the audit and next year's drift will see.

grey = no model yet (eval only)  ·  coral = edge cases still leaking  ·  teal = ripple tucked inside the confidence band

The full ladder