Amsterdam's four big clusters send us four fairly different problems, though they converge on the same requirement to explain themselves.
Payments and fintech dominates. Transaction monitoring, sanctions screening and fraud detection all share an awkward property: the system produces enormous numbers of alerts, almost all of which are wrong, and each false positive is a decision about a real person whose money is not moving. Reducing the alert volume is easy and reducing it defensibly is not, because the burden is to show why a case was closed as well as why it was raised. That is the work described under fraud and AML, and the instrumentation it needs under observability.
Logistics around Schiphol brings air freight documentation, customs and handling data, with an emphasis on speed that port work does not have. The document work is similar in kind to a sea freight pipeline and different in tempo; the pattern is the same one described in our logistics pipeline.
Media and adtech arrive with personalisation, content operations and measurement, and with a lawful-basis constraint sitting under all three.
Agrifood and trading businesses bring forecasting against perishable inventory, where the cost of being wrong is asymmetric and rarely modelled as such.
Across all four, the AI Act classification question comes up on its own, usually from the client rather than from us. It is covered under the EU AI Act.