Industrial AI Diagnostic

Industrial AI Diagnostic — 3 Weeks

Identify where AI works, where it doesn't — and why.

We don't start from data.
We start from how your system actually works.

This diagnostic is designed to uncover where AI can drive real decisions — and where it will fail without proper understanding of the domain.

Request Diagnostic
The Problem

Why most AI projects fail

Most AI initiatives start from available data.
But data is only a projection of reality.

Without understanding the system — its states, transitions, and failure modes — models optimize noise instead of decisions.

The Approach

Outcome → Domain → Feature

We reverse the typical AI process:

1

Start from decisions

Define the outcomes that matter — before touching the data.

2

Understand the system

Map the domain: states, transitions, failure modes, and constraints.

3

Extract the right signals

Identify domain-driven features. Not the other way around.

Timeline

What happens in 3 weeks

W1

System Reality

  • Map processes and decision points
  • Identify failure modes
  • Understand operational constraints
W2

Signals & Features

  • Identify meaningful signals
  • Remove irrelevant data
  • Define domain-driven features
W3

Decisions & ROI

  • Prioritize use cases
  • Estimate business impact
  • Define initial system architecture
Deliverables

What you get

AI Opportunity Map

Top 3 prioritized use cases

ROI estimation

Architecture sketch

Go / No-Go decision support

Engagement

How it works

Duration

3 weeks

Format

Focused engagement with direct interaction with technical and operational stakeholders

Price

Custom pricing
(typical range €15k–€25k)

Context

When data is not enough

In some systems, data alone is not sufficient to make reliable decisions.

For example, in wind energy, monitoring systems may detect anomalies without providing physical confirmation.

In these cases, a validation layer becomes critical.

Get started

Start with clarity

Before building models, understand your system.