RAG · Corporate LLM · Knowledge AI
Company knowledge only becomes valuable when it can be reliably retrieved.
RAG connects processed company knowledge with AI. The crucial step begins before that: capturing data channels, assessing quality, and clarifying responsibilities for the knowledge base.
PDFs, Wikis, Projects
Roles and Permissions
+
Large Language Model
Verifiable and testable
Use in process
The most important step
RAG starts before the chatbot: at the knowledge base.
In many companies, valuable knowledge is spread across PDFs, project folders, emails, wikis, CRM systems, specialist applications and informal processes. Before building a RAG system, these channels and the current operating environment must be understood.
Only when relevance, timeliness, permissions, and responsibility have been clarified can a corporate LLM provide reliable answers. This preparation is not a secondary step, but the foundation of the entire system.
Before the RAG pilot
The knowledge landscape must first become decidable.
Data & AI readiness captures the relevant channels, evaluates the sources, and determines what processing and governance are necessary for reliable AI access.
Make documents, systems, interfaces, and informal knowledge pathways visible.
Clarify quality, timeliness, redundancies, rights, and responsibilities.
Define structure, metadata, maintenance, and a realistic pilot scope.
Knowledge is distributed
Documents, project materials, and expertise are spread across multiple systems and are difficult to find.
Answers must be verifiable
AI answers need sources, context, and clear boundaries, not just well-articulated text.
Teams should be relieved
Research, summarization, and preliminary review are repetitive and consume valuable specialist time.
System logic
A RAG system is built on a curated knowledge base.
Quality arises from the interplay of data preparation, permissions, chunking, retrieval, prompting, response logic, and monitoring. The language model is just one component in this chain.
Structure · Metadata · Versions · Responsibilities
Permissions · Quality · Versions · Limits
Retrieval · LLM · Prompts · Source Reference
Answers · Summaries · Exams · Actions
RAG-Pilot
A good pilot answers the right questions first.
After the readiness phase, the pilot tests a clearly defined knowledge domain. The goal is reliable evidence of which sources work, which questions matter and where measurable workload reduction is possible.
Define Use Case
What questions need to be answered, for whom, and with what business benefit?
Check sources
Realistically assess data quality, recency, permissions, and structure.
Build a prototype
Implement a focused RAG pipeline with test questions, source attribution, and clear boundaries.
Decide
Measure results and derive roadmap, integrations, and next expansion from them.
Fields of application
Where RAG can show results particularly quickly.
Why Trixner?
RAG requires technical depth and process understanding.
The crucial difference lies not in the chosen language model, but in how knowledge is prepared, found, limited, and embedded into existing work.
Trixner Digital Solutions provides support from the initial potential analysis through to a robust RAG pilot and subsequent integration into internal systems.
Next step
Let's check if RAG makes sense for your business.
In the discovery call, we assess data channels, the current state and maturity level. This shows whether data preparation, readiness support or a RAG pilot is the right next step.