· Applications · Claim-safe

Pre-model pipeline signal stabilizer (industrial AI / PdM)

Restates locked Evidence / workbench numbers only. Not universal performance.

Industrial AI stacks fail when models chase chatter and spikes that never should have entered the feature path. GSRF sits before the model as a soft thermostat—not as a replacement for detectors or controllers.

Where it lives in the stack

  1. Sensor / historian stream (positive magnitude)
  2. GSRF Practical — mid-band calm + peak compression toward normal
  3. Feature store / model / dashboard
  4. Separate detector path if you need fixed-FA events

Why this is not slideware

Buyer language (claim-safe)

“Calm the signal first. Reduce nonsense the model never should have seen.” Do not promise ROI or universal FA reduction.

Verdict

Primary commercial path for industrial buyers. Try: /try. Longer note: Pre-AI sensor hygiene. Pricing: /customers.

Sources: Evidence · customers industrial card · pre-ai note · claim ladder

Questions people actually ask

What is a pre-model signal stabilizer?

A filter stage before inference that reduces mid-band oscillation and peak chase so downstream models get calmer inputs.

Is GSRF only for deep learning?

No — any pipeline that consumes noisy positive sensors can evaluate it. Evidence is characterization-locked, not model-specific ROI.

How do I try it before rewiring production?

Try snippet or gsrf-bench on historical CSV; shadow compare vs EMA.

Related research