RAG-Pilot · Corporate LLM · Knowledge System
Test prepared corporate knowledge before it becomes a large AI project.
A RAG pilot validates a clearly defined knowledge domain. If data pipelines, quality, and responsibility are still unclear, the sensible path begins with data & AI readiness.
clearly defined
checked and maintained
RAG
Check sources
Expand or Stop
Goal
Evaluate answer quality and usefulness based on a sound knowledge base.
RAG projects rarely fail due to the language model alone. The crucial factors are processed sources, permissions, search logic, test questions, and integration into real workflows.
The pilot reduces risk when the data and knowledge landscape is sufficiently understood. If this foundation is missing, the upstream readiness phase first creates clarity.
Pilot architecture
A controlled excerpt is sufficient to answer the important questions.
We select a relevant knowledge domain, define typical questions, check sources, and build an initial RAG pipeline with source referencing and clear boundaries.
Process
From the domain of knowledge to a reliable decision.
Choose scope
A clearly defined knowledge area with relevant user questions.
Check sources
Evaluate documents, timeliness, structure, rights, and potential risks.
Build a prototype
Implement RAG pipeline with test questions, source display, and answer limits.
Derive roadmap
Measure quality and make a clean decision on the next expansion, integration, or demolition.
Result
After the pilot, it's clear whether and how RAG works for your company.
You receive a functional and technical assessment of the data landscape, answer quality, user acceptance and integration readiness.
This turns an abstract AI idea into a concrete basis for decisions for a corporate LLM, knowledge system, or automation pipeline.
Next step
Let's start with a focused area of knowledge.
In the initial consultation, we will clarify which use case is suitable for a RAG pilot.