In a half-day workshop, we analyze together which processes or functions benefit most from AI integration — and which don't. The result is a prioritized list of concrete AI feature candidates with a technical assessment and effort estimate. Digital or on site.
The AI market splits into consultancies that deliver strategy papers and developers who lack product thinking. We come from custom development and build AI features as part of products and platforms, not as standalone islands. That means the AI function fits into the existing architecture and the real workflows instead of existing alongside them. Our experience with LLM integration, RAG systems, and AI API integration comes from projects with real production requirements.
The effort depends heavily on the use case. A simple AI module, such as a RAG-based search, can be built in a few weeks. Complex integrations into existing systems with data pipelines and security requirements take longer. The scope is narrowed down concretely in the discovery call.
A first production-ready AI feature is usually developed in four to eight weeks, given a clear scope and an existing tech stack. Projects with a complex data strategy or extensive system integration take correspondingly longer.
That depends on the use case. We work with OpenAI (GPT-4o and others), Anthropic (Claude), open-source models such as Llama, and, where it makes sense, locally hosted solutions for GDPR-critical scenarios. We make the model decision together, based on requirements, not preference.
Consulting delivers an analysis and recommendations. We deliver the implemented function — the AI feature that runs in production. Consulting can be useful as a preliminary step; our focus is on implementation.
In most cases, yes. We start with a short architecture check to assess how the AI module can be connected. The decisive factors are existing APIs, data access, and infrastructure.
Retrieval-Augmented Generation means a language model answers based on company documents or data instead of drawing from its general training. It's the right approach when the AI function needs to reproduce company-specific knowledge precisely. For generic tasks, it isn't strictly necessary.
Yes, completely. Code, prompts, configuration, and all access credentials are handed over. There is no dependency on us as the sole operator of the system.
Many AI features work via AI API integration — interfaces to model providers or internal systems. Interface development is a distinct part of AI integration that we build in parallel or upfront. More on this on our API integration page.
