We build AI agents that work on an assignment and choose suitable next steps. They retrieve knowledge, use approved tools and check intermediate results. You define what they can do themselves and when they need to ask.
Discuss your agentFor example: an agent prepares a customer case for a meeting. It finds documents, compares details and checks what is missing. The next search or action depends on what it has found so far.
Understand the task. The agent establishes the goal and identifies which information it still needs.
Use the right tools. It chooses a suitable search or function and checks the intermediate result.
Review the result. It asks when details are missing. Important actions need approval.
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.First the task. Then the tools.
Then the agent.
01
What should the agent handle? We agree on the input, expected result and boundaries. We check where it needs to interpret information and prepare decisions. Fixed rules are often enough for routine steps.
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We organise data, access and available functions. Where useful, we connect approved documents and instructions in an AI knowledge base. The agent retrieves information relevant to its task. We agree on permissions, sources and approvals before launch.
A bounded task, with scoped access.
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We connect the model, knowledge and tools. With sample tasks, we check its choice of steps and the final result. Tests include conflicting sources, missing details and actions outside its permissions. It should ask or stop when it lacks a sound basis to continue.
Review criteria come from your actual work.
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Your team sees which sources and tools the agent uses and reviews important results. We set limits on runtime and model usage. Further tasks follow once we have assessed the results of its first deployment together.
A fixed price for each phase, agreed before it starts.
AI agent development starts with a bounded task. Data access, tools and review requirements determine the work involved. You know the price before each phase starts.
30 minutes
What should your agent handle? We discuss the information, systems and boundaries it needs.
Discuss your agentAn AI agent works on an assignment using a language model, information and approved tools. It chooses next steps based on intermediate results. To prepare a meeting, it might find documents, compare details and list open questions. Permissions, stopping rules and approvals limit what it carries out on its own.
An AI knowledge base makes approved documents, instructions and common questions available for AI-assisted search. Employees can ask questions in their own words. An agent retrieves relevant passages and uses them to prepare an answer with source references.
We agree on which content to include, who maintains it and who can access it. When information is missing, the system should flag unresolved questions and hand them to a person. An AI knowledge base can support an internal assistant or an AI chatbot.
Yes. We build AI chatbots that answer questions using approved company knowledge and hand enquiries over to employees. A chatbot provides the conversation interface. An AI agent can retrieve information or perform further tasks in your systems behind it. Together we define knowledge sources, permissions and handover rules for unresolved questions.
When every step is defined, rule-based automation is often the better fit. An agent can help when it needs to interpret information and choose suitable next steps. We assess this before development. More autonomy is not a goal in itself.
Our existing projects include research and operational tasks in a custom CRM, plus an SEO agent that analyses and implements changes in a website project. We assess other tasks against your data, tools and requirements. These cases do not prove that every task can be automated successfully.
We check available interfaces and permissions. An agent can use functions that we can connect safely and make traceable. Missing data or connections become part of the scope. Replacing your entire software setup is not a requirement.
We scope tools and permissions, define approvals and test failure cases. In the MatheCode project, a second agent reviews changes before publication. We agree on the human review and other checks each agent needs. We do not promise error-free operation.
The task, data access and required tools determine the cost. We agree on a fixed price per phase. Model usage, operation and ongoing support are separate. A useful estimate starts with a clearly defined first task.
A first usable solution usually takes six to ten weeks. The actual schedule depends on access, scope and review requirements. We begin with a bounded task and agree on additional work after the first deployment.
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.
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