Understand the AI we integrate.

What it is, in one sentence

When a neural network learns, it constantly computes averages. sWELU corrects a subtle error in that average — an error that prevents small models from learning as efficiently as the very large ones.

Why it matters in industry

A 100-million-parameter model equipped with sWELU can learn things we used to think were reserved for multi-billion-parameter giants. Practical for the edge (cameras, sensors, robots) where memory is tight.

What it is, in one sentence

When an industrial machine drifts from its setpoint — say, a filler meant to deliver exactly 75 cl —, you need to decide how much to correct. Correct too much: the machine oscillates. Correct too little: defects pile up. Our patent computes the right dose of correction from two ingredients: the probability that the deviation is a real drift (Weibull over the last 50 measurements) and the Kelly criterion to size the intervention.

Why it matters in industry

Less scrap, less variability, without an expert operator constantly arbitrating. The system distinguishes measurement noise from real drift on its own and intervenes only when statistically justified.

What it is, in one sentence

Extension of B1. Instead of holding a fixed setpoint, the system adjusts the setpoint itself based on the quality loss measured on produced parts (Taguchi loss). The machine learns to re-center itself throughout production.

Why it matters in industry

Shop-floor parameters drift — ambient temperature, tool wear, material batches. The system re-centers without human intervention. Result: fewer silent failures, stable quality even when the environment shifts.