Data Readiness · Knowledge Architecture · Corporate AI
Make Knowledge Safe and Dependable for AI Use.
We map relevant data channels, assess quality, access rights and ownership, and derive a prioritised action plan for knowledge systems and connected processes.
Systems and Sources
Structure and Timeliness
Owner and Rights
Questions and Processes
The crucial step before
AI knowledge systems (RAG) are only as good as the approved content they can use.
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.
When
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.
When
Knowledge is contradictory
Versions, metadata, and timeliness differ, while outdated documents remain discoverable.
When
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 before an internal AI knowledge system can start responsibly.
01
Capture data channels
Make sources, systems, formats, interfaces, and informal knowledge pathways fully visible.
02
Assess the current situation
Classify quality, timeliness, redundancies, permissions, responsibilities, and risks.
03
Prepare knowledge base
Define structure, metadata, maintenance processes, and rules for reliable access.
04
Derive roadmap
Establish prioritized measures, suitable use cases, and a realistic RAG pilot.
What You Receive
Transparency about data, responsibilities and the required preparation effort.
You know which knowledge areas are already viable, where preparation is needed and under which conditions a pilot can responsibly begin.
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.
Typical Project Framework
Knowledge Domain, Systems and Participation Are Defined before the Project Starts.
The effort depends on the number of sources, systems and stakeholders involved. After the discovery call, you receive a written proposal with a schedule, cost framework and clear deliverables.
Assessment Scope
Defined Knowledge Domain
Relevant sources, systems, user questions and deliberate exclusions form the agreed assessment scope.
Client Involvement
Business and System Owners
The project requires contacts responsible for content, data access, permissions and existing maintenance processes.
Outcome
Data Map and Action Plan
You receive a readiness assessment, prioritised actions and a sound recommendation for the next step.
Project Proposal
Schedule and Costs Up Front
Duration, dates, deliverables and costs are agreed in writing after the scope has been defined.
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.