We build the software where AI works for your business.
Three parts, one system for mid-sized businesses: the software your operation runs on. The data from every system in one place. And AI agents that do real work on top of it.
Discuss a projectClaude, ChatGPT and the other models can do remarkable things today.
But they do not know your customers. They do not understand your processes. They do not know your rules. And they have no reliable access to the systems your business runs on.
That is where we start.
Bring the data into one place.
End double entry.
Make company knowledge accessible with an AI knowledge base.
Connect the systems to each other.
The system first. Then the automation.
Then the agent.
01
How does the work actually get done, which rules sit behind it, and where does time go missing? That comes first, not the technology.
02
Standard software is good for standard processes. Your decisive process usually is not one. So we build the system that maps it: a CRM, an internal tool, a customer portal, or a layer between the systems you keep.
The software adapts to the business, not the other way round.
03
Good AI needs context. The relevant information usually exists already, it just lives in different places: in existing systems, in databases, in documents, or in workflows that only a few people understand.
One company. One context that makes sense.
04
Then they can do real work: find information, prepare cases, update data, operate systems, run workflows.
Not as a chatbot beside the company, but as part of a system that was understood and built first.
The people who build work directly with the people who know the process. No long chain of workshop, concept, handover and development.
We walk through the real workflow, understand the rules behind it, and build directly against them. Feedback does not pass through three teams, it lands straight in the software.
No translation layer between business and builder.
Make the workflow, data and rules clear.
Build your system. Refine it together.
Your team uses it. AI agents work within it.
The goal is not a proof of concept. The goal is production.
We connect your tools and build the rules. AI agents handle the work within them.
I did not want a tool that dictates how I work. Today the system maps my workflow, and the AI moves within it inside clear limits.
Built in the project
Enrico BaronettiIndependent regional manager (HGB)Postbank Finanzberatung AGQuotes translated from German.Today I can describe what the site should do myself, and it gets built. Before, I would have needed someone every single time and paid a lot of money.
Built in the project
Niruchan ThanabalasingamFounder & Managing DirectorMatheCodeQuotes translated from German.We build for companies that really want to change something, decide fast and turn speed into an advantage.
Next step
Let us find out together where AI creates the biggest leverage in your company. From Meerbusch near Düsseldorf, for businesses across Germany and beyond.
Discuss a projectAs an AI development agency, we build software, connect data and put AI agents to work within your workflows. First, we identify where your business needs support. Then we build the right solution: a custom system, an integration between existing tools or an agent for a specific task. The people in the conversation write the code afterwards. The first usable version is usually ready after six to ten weeks.
AI agents for business are programs that use a language model to complete a task on their own: pull information from your systems, work out the next steps, operate tools, deliver the result. In practice that means, for example: read a customer enquiry, find the matching case in the CRM, draft a reply, and only touch the data that has been cleared for it.
If you want us to build an AI chatbot, we can connect an agent to a conversation interface. An AI knowledge base makes approved documents and instructions searchable. The agent answers questions using your company knowledge and hands unresolved enquiries to your team. Before launch, we define data access, permitted actions and review steps.
No. In most cases what works stays. We connect the existing systems and build new only where something is missing. Often that is the layer in between, not the individual tool.
The first usable version is usually ready after six to ten weeks. We build in stages that each deliver value on their own, not for a year on something that only runs at the end.
That depends on the scope and can only be answered seriously after the first conversation. What we can commit to: a fixed price per stage, before the stage begins.
Usually in a data centre in Germany or the EU, in accounts that belong to your company and not to us. You grant access and can revoke it at any time. What goes to an AI model at all is decided per class of data: a customer conversation with personal data is a different thing from a product description.
For the usual route, yes: a business account with the model provider under a data processing agreement per Art. 28 GDPR. At Anthropic, inputs and outputs from the commercial products are not used for model training by default. The enterprise tier adds a configurable retention period and a selectable region, so processing runs through AWS, Google Cloud or Microsoft in the EU instead of the default infrastructure in the US.
If processing still goes to the US, the transfer needs a legal basis that the provider has to supply. That assessment is your data protection officer's call, not ours. We supply the contractual position and the documentation it rests on.
Yes. Open models run at providers with data centres in Germany or the EU. Then there is no third-country transfer to justify, and the contract is an ordinary processing agreement with a European provider. These models are weaker than the closed frontier models, but for clearly scoped tasks in a business that is often enough.
Usually yes. A CRM holds customer data, but it rarely knows your workflows, your rules, and the information from the other systems. That is exactly the gap we close.
Then we say so. The foundation of connected data and a system that fits your workflows carries its weight without AI. It is the part that has to be built first anyway.