Putting generative AI
to work in your company

Generative models turn an input into new content: images, videos, text or structured suggestions. In a company the benefit rarely comes from the model alone. What matters is the framework around it: which data may go in, who reviews results, where they end up and what a run costs.

The difference between an experiment in the browser and use in daily operations lies in exactly that framework. We work on both ends: we produce content ourselves and we build the systems in which your team produces it.

Which areas pay off depends on your business. These five come up most often.

A pilot proves little if it runs outside the real process. That is why we work in this order.

With generative AI, data flows and responsibilities are part of the design. That includes which data a model may see, where processing takes place, who has access to inputs and results, and how content is labelled, for example under the EU AI Act.

The available operating models and how to assess them are described under Data-sovereign generative AI. This does not replace a legal assessment, which belongs with your data protection and legal advisers. We make sure the technical implementation can reflect the requirements.

A tool that sits next to your existing systems is rarely used for long. Results have to arrive with their metadata where they are needed, for example in the DAM, the PIM or the CMS, and jobs must be startable from the processes you already have.

What this looks like is described under Integrating generative AI into existing systems.

Trickhouse is a generative AI agency in Düsseldorf. We produce content with digital twins and AI models, build workflows and agents, connect them to your systems and train your teams. We prefer open-source software and open-weight models because they keep control over models, data and processes with you.