We Build Your AI Solution
We design and build applications that deliberately use large language models (LLMs), retrieval-augmented generation (RAG) and agentic frameworks, embedded into existing architectures, data landscapes and processes. We don’t just master the “new” tooling, we bring genuine AI engineering: methodically and conceptually mature solutions that stay reliable and maintainable in production environments.
Overview
Our Offering
We build AI solutions for you or work directly with you in mixed teams. From the first idea to operation, we bring our experience to bear and ensure conceptually mature, well-integrated solutions.
Our Expertise
We have spent years working on the right use of generative AI in enterprise contexts. What sets us apart: we combine generative AI with solid architectural craftsmanship, for solutions that don’t just work, but stay reliable and maintainable in production.
Typical Occasions
You want to build or offer GenAI-based software products, either as an internal tool to support developers, managers or case workers, or as an external product that brings real value to your customers.
Our Offering
Small AI experiments quickly deliver good results. However, productive and professional use of intelligent systems in companies requires more than a good experiment. We support companies from the first idea to a production-ready AI solution, with a process based on hypotheses, validation and clear architecture principles.
00
Potential & Hypotheses
Together we define meaningful use cases, measurable goals and initial hypotheses about feasibility. We assess data availability, risks and business value, and set criteria for later success evaluation.
01
AI Product Design & Evaluation Setup
We design the solution as a product. We define interactions, data flows, security concept and governance. In parallel, we develop an evaluation concept with suitable metrics (e.g. response quality, retrieval precision, usage feedback) to make progress and effectiveness measurable.
02
Proof of Concept
In a focused PoC, we implement the riskiest system components and validate the viability of our approach. Especially in the AI context, it’s hard to predict without a PoC whether model choice, context preparation, retrieval strategies and agent designs will work well enough.
03
Development and Production Build-Out
During implementation, we extend components step by step while keeping an eye on latency, cost, data protection and integration capability. We continuously test against real-world scenarios for important quality aspects such as robustness, maintainability or explainable behavior.
04
Operations and Further Development
We establish monitoring, feedback and retraining processes to observe model and system behavior over the long term. This keeps the system controllable, extensible, and able to keep learning.