Operations and marketing
From Data Chaos to Competitive Advantage.
Knowledge workers spend a considerable share of their working time searching for internal information. 42% of organizations view the loss of employee knowledge due to turnover or retirement as a major risk to their operations.
Retrieval-Augmented Generation (RAG) is an AI approach that directly addresses this problem by specifically accessing your internal knowledge before formulating an answer. This provides every employee with immediate access to precise and source-based answers.
Here's a visual representation of the architecture, from query to intelligent, source-based answer:
A user asks a question using natural language.
Here's a visual representation of the architecture, from query to intelligent, source-based answer:
The retrieved information is combined with the original question to create an augmented prompt.
The Language Model (LLM) formulates an answer based on this enhanced prompt.
Large Language Models (LLMs) often suffer from outdated knowledge and can't access private data, leading to inaccurate or fabricated answers. Retrieval-Augmented Generation (RAG) solves this problem by searching an external knowledge base, such as your company's documents, for relevant facts before formulating a response. These retrieved facts are then provided to the LLM as precise context for its answer.
Instead of "hallucinating," the model generates an accurate, up-to-date, and trustworthy statement. This makes AI truly secure and usable for specific, data-driven tasks.

The first step involves consuming various types of documents, including:
Breaking down content, capturing meaning
Granting access to knowledge
1. How long does it take to implement a RAG system?
We typically achieve an initial Proof of Concept (PoC) within 6 to 8 weeks. A fully integrated, production-ready system for daily use (Go-Live) is often achievable within 6 months. The exact duration depends on the complexity of your data and the depth of integration required.
2. What's the difference between a PoC and Go-Live?
A Proof of Concept (PoC) is a lean, functional system with a limited scope. It quickly and cost-effectively demonstrates the fundamental benefits and technical feasibility. Go-Live refers to the deployment of the fully developed, scalable application, integrated into your IT landscape, for all intended end-users.
3. How secure is our data during this process?
The security of your data is our highest priority. We design an architecture that precisely fits your requirements. This ranges from secure cloud services (e.g., Azure OpenAI) to fully self-hosted solutions where your data never leaves your infrastructure. Compliance with GDPR and your internal compliance guidelines is, of course, guaranteed.
4. What data can we use for the system?
Practically all of it. This includes internal documents such as PDFs, Word and PowerPoint files, content from Confluence or SharePoint, emails, and also structured data from databases or CRM systems. We'll help you identify and connect the most valuable knowledge sources within your company.
5. What does a RAG project cost?
Costs are project-specific. They depend on factors such as the number of data sources, the complexity of the data, and the chosen architecture (cloud vs. self-hosted). The initial PoC is a transparent and cost-effective method to validate the benefits before making larger investments for the Go-Live.
We combine modern AI technology with uncompromising security. We develop the right technical solution for your needs, from secure cloud applications to systems that run on your own servers. This way, your data always remains under your control.
We deliver not experiments, but precise, reliable, and secure AI applications tailored exactly to your company's data.
Alexander Haus, AI Developer


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