ML Env

Use cases · 142 of them

What a business can hand to a model.

Every case below runs on our own servers in Amsterdam. Each page says what the system does, what data it needs from you, and where a person stays in the loop — including the cases where the honest answer is that the model will only be adequate.

Customer service

15 cases

Customer service is usually the first place a small or mid-size shop puts a language model to work: the messages are short, the questions repeat, and a person still clicks send. Each page below says what the system actually does, what data you must already have, and where the model stays mediocre.

Search & discovery

12 cases

These use cases are about making things findable: products for your shoppers, answers for your customers, documents for your own team. They all run on the same retrieval stack — multilingual embeddings, a reranker, and a language model where a composed answer is needed — hosted in the EU on our hardware.

Catalogue content

14 cases

These use cases turn data you already hold — supplier feeds, spec sheets, existing listings — into cleaner, fuller catalogue content. All of them run as overnight batches on EU-resident hardware, and all of them assume a person reviews the output before anything goes live.

Selling across languages

16 cases

Selling across EU borders means every customer touchpoint — catalogue, chat, email, reviews, policies — exists in several languages, usually with nobody on the team fluent in all of them. These tools run that multilingual workload on models hosted in the EU, always with a person deciding what actually gets published or sent.

Marketing

13 cases

Words your shop already has to produce — product pages, emails, ads, review replies — drafted on our hardware in Amsterdam instead of by hand. Every page below says what data you need to have, where the model is honestly mediocre, and where a person must stay in the loop.

Sales & B2B

15 cases

Sales work in a small B2B firm is mostly reading, matching and writing: enquiries, RFQs, quotes, tenders, follow-ups. These pages describe where a private LLM stack genuinely helps with that paperwork — and where a person must stay in charge.

Operations & logistics

13 cases

The paperwork side of moving goods: supplier emails, order confirmations, carrier notifications, price lists and warehouse notes. These cases read the documents you already have and turn them into queues, tables and briefs a person can act on — nothing here acts on its own.

Finance & admin

15 cases

The paperwork side of running a business: supplier invoices, receipts, bank reconciliation, contracts and the shared finance mailbox. Every case here drafts, sorts or finds — a person still approves anything that touches the ledger, a customer or a signature.

HR & internal

14 cases

Most of the text inside a small company — handbooks, handovers, job ads, onboarding notes — is written and answered by the same few overloaded people. These use cases take the routine drafting and looking-up off them, while every decision that touches an actual person stays with a person.

Voice of customer

15 cases

These jobs read the feedback your customers already write — reviews, support tickets, surveys, cancellation notes — and turn it into something you can count, read and act on. None of them replace a person reading the important bits; they decide which bits are worth a person's time.

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Where we test it

Two A100 servers in a NorthC datacentre in Amsterdam. Whatever a project actually needs is sized to it.