Svelaro

Know what to change.

Manufacturers sit on years of sensor data they barely use.

Defects are a daily reality, and the response is a meeting, a guess, a tweak — and the problem returns next week.

It gets harder still when the veteran who set the recipes retires, and the knowledge leaves with him.

Svelaro changes that.

What Svelaro does

the actual root causes

Discover which process parameters are really driving your defects — validated on your own data, not guessed from patterns.

the next defect window

See when quality is about to drift, before it hits the line.

the safe operating range

Get concrete operating ranges for every critical parameter. Keep the line inside the safe zone and defects stay down.

Evidence

Three public industrial datasets whose true causes are documented. Six methods on identical data, settings fixed before any scoring.

6 of 9

problems where the first variable Svelaro names is the true cause, from a list of two. Best other method: 3 of 9.

0 of 7

consequences of the target returned as causes on an industrial penicillin process. Correlation and Granger: all 7.

public plant datasets: a simulated chemical plant, a batch bioprocess and an operating cryoplant.

When defects cost too much to scrap and move on — Svelaro tells you what to change.

Svelaro excels when at least two of these are true:

Scrap runs over €500k/year, or a single defect costs you €10k+ by the time it's caught
Your process has 20+ sensors that interact (continuous or batch)
When a defect happens, you can't trace the cause by walking the line

Same engine. Your data. Your answers.

Reaction parameters, batch quality, yield consistency

Mechanical properties, yield loss, process stability

Batch consistency, yield from variable inputs, process stability

Recyclate variability, output quality, yield from mixed scrap

Cure cycle stability, void reduction, part-to-part consistency

Dimensional consistency, cycle stability, scrap reduction

Think Svelaro might fit your line?

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