Engineering solution
The Connected Factory Data & AI Pilot connects a defined set of machines to a reliable data foundation — collection, storage, context, and dashboards — and then applies analytics or AI to one specific production decision. The pilot is scoped around a measurable question, so success or failure is visible in weeks, not promised in years.
Most industrial AI initiatives fail before the model stage because the underlying data is missing, untrusted, or lacks context. A vibration alert means nothing without knowing which product was running and whether the machine was in changeover.
This pilot builds the foundation first: machine connectivity, contextualized storage, and dashboards operators actually trust. Only then is an analytics or AI layer applied — to a single decision your team chooses, with acceptance criteria agreed before work begins.
Scope
Project process
Agree on the single production question, its baseline, and what evidence would justify scaling.
Establish machine connectivity and contextualized storage for the pilot scope.
Deliver dashboards so the team can verify the data is right before any modeling.
Implement the analysis layer with documented methods and limitations.
Compare results against acceptance criteria and recommend scale, adjust, or stop.
| Pilot type | What success looks like | How it is measured |
|---|---|---|
| Visual inspection | Catches the defect class that escapes sampling, at line speed | An agreed defect set, measured against manual inspection over an agreed period |
| Predictive maintenance | Flags a developing fault far enough ahead to act on it | Lead time on known historical failures, plus the false-alarm rate |
| Knowledge assistant | Finds the right passage faster than searching by hand | Answer accuracy over a fixed question set, checked by the people who use it |
| Production analytics | Numbers that reconcile with the production meeting | Agreed metric definitions, matched against manual counts |
We do not publish fixed prices: these are the factors that determine effort. A scoped proposal follows the technical fit review.
A pilot can conclude that the data does not support the use case. That is a valid and comparatively cheap outcome, and it is stated as a result rather than converted into a longer engagement.
Last reviewed by Geek Engineering senior engineering review.
At minimum: reliable collection from the machines involved, storage with timestamps and production context (product, shift, machine state), and enough history to represent normal operation. Without context, models correlate noise. The pilot builds exactly this foundation before any AI is applied.
One bounded decision with a measurable baseline — for example, classifying the true causes of downtime on one line, or flagging anomalies on one critical asset. Safe pilots run alongside existing controls and advise people; they do not take control actions until they have earned trust.
Yes. If a historian, SCADA, or MES already collects useful data, the pilot builds on it rather than duplicating it. The assessment step maps what exists, what is trustworthy, and what gaps must be filled for the chosen decision.
No. The connectivity, storage, and dashboards remain a permanent data foundation regardless of the AI outcome. That is deliberate: the pilot is structured so the non-AI value stands on its own.
Describe your equipment, objectives, timeline, and constraints. We will confirm whether this solution fits — and say so plainly if it does not.
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