Retrieval-Augmented Generation (RAG)
For compliance-compliant AI answers
Your benefits at a glance:
What is RAG — and why is LLM alone not enough?
Combine generative AI with your internal company data — whether structured in knowledge databases, data warehouses (DWH), data lakes, or unstructured in documents such as PDFs or ticket systems. Retrieval-Augmented Generation (RAG) combines a language model (LLM) with an intelligent semantic search that integrates relevant and up-to-date information from your internal sources in real time. As a result, your AI systems provide answers with verifiable source and time stamp — they don't speculate, but prove.
How does RAG work technically?
Vectorization of the request
The user input is converted into a semantic vector.
Retrieval from internal sources
Similar content is found using a vector search (e.g. FAISS, Weaviate) from structured or unstructured data (e.g. PDFs, CRM data, wiki articles).
context enrichment
The content found is given to the language model as an extended context (context injection).
Response generation
The LLM generates a well-founded answer based on this contextual data.
What is RAG — and why is LLM alone not enough?
Combine generative AI with your internal company data — whether structured in knowledge databases, data warehouses (DWH), data lakes, or unstructured in documents such as PDFs or ticket systems. Retrieval-Augmented Generation (RAG) combines a language model (LLM) with an intelligent semantic search that integrates relevant and up-to-date information from your internal sources in real time. As a result, your AI systems provide answers with verifiable source and time stamp — they don't speculate, but prove.
The most important advantages in business use
Where RAG provides real added value in the company
data integration
With the help of an RAGs, you can link your company data with the capabilities of language models without explicit training.
Knowledge management & internal search
Reliable answers to questions directly from all internal data sources.
Dynamic automation based on your company data
RAG systems are an integral part of the dynamic controls of modern automation architectures.
This is how we integrate RAG into your existing architecture
Our RAG solutions can be precisely embedded into your existing IT and data architecture — flexibly as an API, via existing chat front ends or deeply integrated into operational systems. Instead of rigid templates, we rely on modular architectures that are based on your infrastructure, security requirements and use cases.
Existing data sources
e.g. DWH's, data lakes, PDFs
Existing front ends
e.g. intranet, support portals, internal tools
system landscapes
such as CRM, ERP, or internal APIs
1.
Analysis of your data landscape & access channels
2nd
RAG component architecture design including authentication, data access, vector storage, LLM connection
3rd
Proof of concept on real company data
4th
Step-by-step integration & optimization — with a focus on monitoring, scalability, and security
Our solutions are cloud, on-prem, or hybrid — depending on your compliance requirements. We provide not only technical support, but also methodical support — from governance issues to integration into existing processes.
Frameworks & tools that we use for RAG
LangGraph
For flexible orchestration of complex retrieval and response flows, including status management
FAISS/Weaviate
For scalable vector indexes via unstructured sources (e.g. PDFs, wikis, emails)
Llama2, Mistral, Claude
Customizable depending on licensing model, data protection requirements or computing environment (local vs. cloud)
Python/LangChain
For rapid backend integration, custom RAG pipelines, and tool execution (e.g. SQL, APIs, internal functions)
chunking strategies
for better retrieval precision This involves dividing long texts into precise sections.
Token optimization & caching
to reduce costs and response time
Start now — with a specific RAG use case
Would you like to know how RAG can be used specifically in your organization? Let's develop a use case together — from the idea to the first ready-to-use agent.
Thomas Howert
BI consultant and AI expert
Test your own AI system in just two weeks!
FAQs about retrieval augmented generation
Retrieval-Augmented Generation combines language models (such as GPT) with a semantic search in your internal company data. This gives you AI-generated answers that are up-to-date, accurate and comprehensible — with a clear origin and reference to the source.
Classic language models answer questions exclusively with trained information, which is often outdated or unspecific. RAG, on the other hand, enhances every AI response with up-to-date, relevant information from your internal sources. This ensures significantly more accurate, reliable results in everyday business.
Yes RAG is integrated directly into your existing IT infrastructure so that sensitive data never leaves your company. Your data sovereignty is maintained and you meet the highest data protection, security and compliance requirements.
RAG integrates seamlessly with a wide range of systems, including:
- CRM systems (e.g. Salesforce, HubSpot)
- ERP systems (e.g. SAP, Microsoft Dynamics)
- knowledge management systems (e.g. Confluence, SharePoint)
- cloud storage (e.g. AWS, Azure)
- document management systems (DMS)
- APIs and custom databases
Integration is carried out flexibly via proven interfaces, adapted to your professional and technical requirements.
Yes, RAG is easy to integrate with existing chatbot platforms such as Microsoft Teams, Slack or internal web portals. Users therefore always receive up-to-date, data-based answers directly from their corporate sources — for faster decisions and a significantly improved user experience.