We develop your prototype with real data and real interfaces, not just on paper. Together, we evaluate the quality of the results and recognize early on whether the approach can be scaled up. This allows you to quickly see whether AI is effective in your context - or whether another approach makes more sense.
We combine language models with your data so that answers are not just plausible, but correct. Retrieval Augmented Generation (RAG) combines internal knowledge with generative AI - comprehensible and up-to-date.
Chatbot
We build chatbots that really help: with access to your data and clear boundaries. This creates dialog systems that understand what is meant - not just what is asked.
Evaluation
We measure how well your prototype works. With real usage data, clear KPIs and manual checks, we show whether the quality and benefits are right.
Guardrails
We ensure that your system remains secure. We use defined rules, filters and test mechanisms to control what AI is allowed to do - and what it is not.
A prototype shows whether an idea works before you invest a lot of time and budget. It makes assumptions verifiable and provides a basis for decisions. This allows you to recognize early on whether a use case is technically and organizationally feasible.
How does the development work?
We define the goal, check the data situation and build a functioning prototype. This is tested with realistic scenarios in order to make the effects and limitations visible. The result is a clear decision on whether and how the model should be developed further.
How much data is needed for this?
Existing or synthetically generated data is often sufficient to simulate realistic behavior. If data is missing or incomplete, we help to set it up. It is important that the test remains meaningful, even without complete data sets.
What technologies do you use?
We use TensorFlow, OpenAI, Claude, Perplexity, Open WebUI and our own AI shells based on shadcn. Which tools are used depends on the goal and the environment. It is crucial that the prototype remains comprehensible and can be cleanly integrated later on.
What happens after the prototype?
If the result is convincing, it can be transferred to productive operation. To this end, we add interfaces, security and monitoring. If it turns out that an approach is not worthwhile, the result is still valuable because it provides clarity.
