Retrieval-Augmented Generation (RAG)
Compliance-Driven AI Responses
Key Benefits:
What is RAG, and why isn’t an LLM alone sufficient?
Combine generative AI with your internal enterprise data—structured sources such as knowledge bases, data warehouses (DWH), and data lakes, or unstructured sources like PDFs and ticket systems. Retrieval-augmented generation (RAG) integrates a language model (LLM) with intelligent semantic search, retrieving relevant and current information in real time from your internal systems. Your AI thus delivers answers with traceable sources and timestamps, ensuring transparency instead of speculation.
How does RAG work technically?
Query vectorization
User inputs are converted into semantic vectors.
Retrieval from internal sources
Relevant content is identified via vector search (e.g., FAISS, Weaviate) across structured and unstructured sources like PDFs, CRM data, and wikis.
Context enrichment
Retrieved content is injected into the language model's context.
Answer generation
The LLM generates informed responses based on enriched context.
Key business benefits
Real business value of RAG
data integration
Connect your enterprise data to language models without retraining.
Knowledge management & internal search
Reliable real-time answers from all internal data sources.
Dynamic automation based on enterprise data
RAG forms the foundation for smart automation within your architecture.
How we integrate RAG into your existing architecture
Our RAG solutions fit flexibly into your existing IT and data landscapes—via API, chatbot frontend, or deep integration into operations. We build modular architectures that match your infrastructure, security requirements, and goals.
Existing data sources
E.g. DWH's, Data Lakes, PDFs
Existing frontends
E.g. intranet, support portals, internal tools
System landscapes
Such as CRM, ERP, internal APIs
1.
Analyze your data landscape and access points
2.
Design RAG architecture including auth, data access, vector storage, and LLM
3.
Proof of concept using real enterprise data
4.
Stepwise rollout with monitoring, scaling, and security
We support cloud, on-prem, and hybrid setups in full compliance with your internal governance. From strategy to process integration—we guide you both technically and methodologically.
Frameworks and tools used for RAG
LangGraph
Flexible orchestration and state management for retrieval workflows
FAISS/Weaviate
Scalable vector indexing of unstructured data (e.g., PDFs, wikis, emails)
Llama2, Mistral, Claude
Tailored to license, privacy, and infrastructure requirements
Python/LangChain
Fast integration, custom RAG pipelines, and tool execution (e.g., SQL, APIs)
Chunking strategies
Increase precision by segmenting long texts
Token optimization and caching
Reduce latency and cost
Get started now – with a specific RAG use case
Curious how RAG can benefit your organization? Let’s define a use case together—from concept to operational agent.
Thomas Howert
BI consultant and AI expert
Test your own AI system in just two weeks!
FAQs about RAG
RAG combines language models (like GPT) with semantic search of internal enterprise data to deliver accurate, up-to-date, and source-based AI responses.
Traditional LLMs rely on pre-trained knowledge, which may be outdated or generic. RAG enhances them with real-time, enterprise-specific information.
Yes. RAG is deployed within your IT landscape, keeping sensitive data internal and compliant with security and data protection standards.
RAG integrates with:
• CRM systems (e.g., Salesforce, HubSpot)
• ERP systems (e.g., SAP, Microsoft Dynamics)
• Knowledge platforms (e.g., Confluence, SharePoint)
• Cloud storage (e.g., AWS, Azure)
• Document management systems (DMS)
• APIs and custom databases
Yes. RAG works seamlessly with platforms like Microsoft Teams, Slack, or internal portals—providing real-time, data-driven responses that enhance decisions and user experience.