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Commercial evidence · NPNT

PROMCYCLE AI: industrial AI in a shadow pilot

An architecture that connects production data, an AI hypothesis and a measurable KPI without changing a validated process.

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4sector scenarios
10+language versions
4shadow-pilot stages
01

The problem

In biotech and food production, data is split across equipment, logs, laboratories and quality systems, so deviations are often seen too late.

02

Why the old process was costly

Manual reconciliation consumes expert time, is hard to reproduce and does not provide a safe way to test a model before it can affect the line.

03

What NPNT built

NPNT designed a multi-page sector platform, a data map, a four-stage shadow-pilot workflow and gates for moving to limited implementation.

04

Timeline

The public prototype timeline is not disclosed. A real pilot timeline is set only after a data audit and selection of one production line.

05

How the result is measured

Model suggestions are compared with actual outcomes without automated control: early-signal accuracy, false alarms, response time and one selected production KPI.

A bounded first stage

Describe the problem—we will outline the architecture of the first step

An initial assessment does not require medical records, trade secrets or a data dump. Process context and the intended outcome are enough.

What to include in the first message
  1. 01The process, product or audience that needs to change
  2. 02The data or materials already available
  3. 03Who owns the decision and which criterion will define success
What happens next

Our first reply will clarify the process, available data, accountable specialist and success criterion. We will then propose a bounded first stage with an indicative timeline and budget.

Describe the problem →