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Artificial Intelligence

AI where the work already happens

We build AI into your existing systems: into the CMS, the shop, the ERP or the ticket system. Without your team having to switch tools for it.

What this is about

Most AI initiatives do not fail on the technology, they fail on the switch. Anyone who has to open a second window for a suggestion, paste something in there and copy the result back will give up after two weeks. That is why we build the feature into the place where the work happens.

In the tool, not next to it

A suggestion right in the editing window gets used. The same suggestion in a separate tool does not.

With your data

AI only becomes useful once it knows your products, policies and processes. That takes a proper connection, not just an account.

Start small

One use case that genuinely works is more convincing than a platform that could do everything.

Anything that needs a second window nobody uses after two weeks.

Situations that typically bring clients to us

  • There are accounts for AI tools, but hardly anyone uses them day to day.

  • People constantly copy and paste between the systems.

  • The suggestions do not know your own products and guidelines.

  • It is unclear which data the model is allowed to see at all.

  • A first attempt petered out because it was too cumbersome.

  • Every department uses a different tool on its own initiative.

  • None of this sounds like you?

    Then tell us what you are actually dealing with.

    Get in touch

What we actually do

Built into the CMS

Suggestions for titles, descriptions and summaries directly in the editing window, following your own guidelines.

Built into shop and PIM

Producing product texts, category texts and translations where the item data is maintained anyway.

Connection to ERP and ticket system

Summaries, suggestions and classifications in the systems where your processes run.

Access to your own content

Connecting your documents and data holdings, so answers rest on your material instead of general knowledge.

Data protection and operation

We clarify which model runs where and which data it is allowed to see, in an environment inside the EU if you prefer.

Support after launch

We look at whether the feature is actually being used and improve it wherever it stalls.

How we work

  • 01

    Choose the use case

    We look for the one place where the benefit is felt immediately, instead of starting broadly.

  • 02

    Check the system

    Which interfaces exist, which data is available and what does the existing software allow?

  • 03

    Build and test

    Implementation in the existing system, tested with real cases from everyday work.

  • 04

    Expand

    Only once the first case holds up in operation does the next one follow.

The first use case decides whether AI takes hold in the company. Tell us which systems your team works with every day.

Let's connect 🚀

Frequently asked questions

Anything that needs a second window nobody uses after two weeks. So AI belongs where the work already happens.

What does AI implementation mean at dxm?

Bringing AI to where the work happens: as a suggestion in the editing window of the CMS, during product maintenance in shop and PIM, as a summary in the ticket system or ERP. That includes connecting your own documents and data, clarifying permissions and data protection, and support after launch. Anyone who has to open a second window for a suggestion and copy results back will give up after two weeks.

Do you only implement ChatGPT?

No. We are not tied to any vendor and choose the model by task, data protection requirements and cost – with processing inside the EU on request. What matters more than the model is the environment around it anyway: which data it may see, which rules apply, how results are checked and what happens when it does not know the answer.

How do we avoid uncontrolled one-off solutions?

By providing an official route that is convenient to use. As long as no usable tool is available, staff keep working with private accounts and nobody knows which data leaves the building. We set up central access with clear permissions, complemented by understandable guard rails: what is allowed, what is not, who decides in case of doubt. For RHEWUM this became an AI cockpit where staff use tools securely and share their own agents.

Does the rollout in the team come with it?

Yes, otherwise the licence goes unused. We show the benefit on real tasks from the department in question rather than on generic examples, we start with one use case that genuinely works, and after a few weeks we look at whether the feature is actually being used. Where it stalls, we improve it – often in the handling rather than the model.

AI accounts in place, but nobody uses them?

Tell us which systems your team works with every day. We will tell you where building it in is worth it.