Builds a verified digital twin
Statistical models learn the factory's real norms and distinguish normal variability from a change that needs action.
Solutions
Every factory follows the laws of nature.Statistical AI makes them visible.Normalic makes them useful.
Normalic learns the factory’s normal behaviour, detects meaningful deviations and gives every role clear findings and actions—no data-science skills required.
The Normalic difference
Finds what matters
AI searches for relevant anomalies and sustained trend changes instead of waiting for the user’s query.
Brings AI into everyday work
Role-specific tools, explanations and triggers help the team act without becoming data-science specialists.
Creates a decision foundation
The verified models support production planning, people management and measurable improvement projects.
Start with what you have
One real production-data sample is enough to begin.
You do not need a finished data platform or a perfect schema. Send an existing CSV, Excel export or API sample and we will map the first production flow together.
Identity
Machine or workstation, timestamp and timezone.
Output
How many units were made during the interval.
Capacity
The normal target rate, when it is available.
Context
Product, operator and shift calendar.
State
Whether the process was running and expected to run.
Interruptions
A practical planned or unplanned stop reason.
From verified models to daily work
Three steps from production norms to better decisions.
Build a verified factory digital twin, bring practical AI tools into the team and run planning and improvement on trusted data models.
Discuss your production challenge →