Data Readiness · Knowledge Architecture · Corporate AI
Good AI starts with your company knowledge.
We do not start with the model. We map data channels, assess the current internal environment and build a dependable knowledge foundation for RAG, corporate LLMs and connected processes.
Systems and Sources
Structure and Timeliness
Owner and Rights
Questions and Processes
The crucial step before
RAG is only as good as the knowledge it's allowed to access.
Organizational knowledge is rarely stored in a single location. It is found in documents, emails, SharePoint, CRM, line-of-business systems, project folders, and in the minds of individual employees. Before AI can access it, it must be clear what information is available, relevant, up-to-date, and trustworthy.
Data & AI Readiness creates this transparency. The result is more than an inventory: it is an actionable plan for data preparation, governance, technical integration and the first high-value use cases.
Data channels are unknown
No one can say with complete certainty which sources are relevant to a knowledge process and how information flows between systems.
Knowledge is contradictory
Versions, metadata, and timeliness differ, while outdated documents remain discoverable.
Responsibility is unclear
Ownership, access rights, shares, and rules for maintenance or deletion are not definitively established.
Readiness Model
From a data inventory to a robust knowledge base.
We don't just consider files. The interplay of channels, professional meaning, quality, permissions, responsibilities, and subsequent user questions is crucial.
Approach
Four Steps to Take Before Launching a Corporate LLM Effectively.
Capture data channels
Make sources, systems, formats, interfaces, and informal knowledge pathways fully visible.
Assess the current situation
Classify quality, timeliness, redundancies, permissions, responsibilities, and risks.
Prepare knowledge base
Define structure, metadata, maintenance processes, and rules for reliable access.
Derive roadmap
Establish prioritized measures, suitable use cases, and a realistic RAG pilot.
Result
A clear basis for decision-making instead of vague AI readiness.
They know which areas of knowledge are viable, where further processing is needed, and under what conditions a RAG or corporate LLM project can be launched responsibly.
Relevant channels, systems, sources, and dependencies at a glance.
Quality, access rights, responsibilities and risks assessed transparently.
Concrete measures for preparation, governance, pilot, and integration.
Next step
Let's start with your actual data and knowledge landscape.
In the initial consultation, we clarify which data channels, knowledge areas, and target processes should be considered first.