Site Assessment: Data and AI Potential

On the most recent project, the customer team spent less than twenty hours in total over three months: twenty minutes of the morning meeting every month to month and a half — process engineering, maintenance, quality, production and management at one table.

Plus one person on your side as the point of contact, one to two hours a week, mainly to check whether the analyses are technically correct. The development is done by someone who can read the data himself — which keeps your effort small.

No. The work is done on the data you have today — and almost everywhere it is a mix: logs from the line controller, sensor values, readings from the X-ray thickness gauge, orders in the ERP system. Much of it is already sitting in backup files without anyone using it.

The opposite approach — first merging everything into one big system — is the expensive one. In the automotive industry, that produced platforms nobody could operate afterward. Where something is really missing later on, it is named: usually one or two additional exports from the controller.

No. The analysis uses what the controller already puts out — the controller is not changed and not operated remotely. If additional exports are needed, your line's programmer sets them up; the effort for that is manageable.

No new sensors and no labels on the parts are needed. The matching runs on what your line already records.

At your site — on a server or a virtual machine in your building. The application collects the data on its own, analyzes it and reports when something looks unusual. In normal operation, nobody operates it.

Opening an analysis does not take an engineer: it is designed for the people who work at the line. One hour of training is enough.

The finished system runs in your building — no AI cloud, no subscription. For the first demo you provide an extract voluntarily: only what you release, with encrypted handover and a confidentiality agreement. It is analyzed on isolated hardware in an access-controlled workspace, not in a third-party data center.

Formulations and process parameters are not needed to find anomalies. What a tool is allowed to see is agreed in writing beforehand — which frameworks stand behind this.

A crusted or out-of-calibration probe does not report itself as faulty. It keeps delivering values — just the wrong ones, and the controller accepts them without question. It often only becomes noticeable through quality, which means late.

It can be spotted earlier: there are measurements from different sources that normally move in the same direction — parts throughput and the conductivity after the rinse, for example. If they drift apart, the analysis flags it and names the most unusual measuring points of the day. That way your people know where a check measurement or a calibration is due — without a single additional sensor.

No — and monitoring that promised this would not hold up in a plating shop. Crusting, mechanical damage and the actual condition of a line show up when someone goes and looks, not in a series of numbers.

What the analysis takes off your hands is the searching: it tells you which spot and which time window to look at first. The judgment stays with your process engineering and maintenance teams — and they get back exactly the time that goes into troubleshooting today.

Through two separate tracks that are compared against each other: the line's values on one side, the manual check measurement on the other — recorded digitally by phone right at the line, instead of on a paper list.

If the difference exceeds a threshold agreed with you, an alert is raised. That proves two things at once: that the measurement was taken — and that the sensors arrive at the same result. For audits, this documents what would otherwise just be checked off.