When product information, prices, documents, and data sources drift apart, the result is inconsistent guidance, wasted search time, and unreliable AI answers. I bring that information into a current, traceable, and approved state. Employees find what applies more quickly, and AI can answer reliably.
25+ years in IT · including 20+ years at Daimler / Mercedes-Benz · hands-on BotCore experience · IHK-certified AI Manager & AI Officer (DEKRA)
We first clarify sources, conflicts, and responsibilities. The right architecture, a testable pilot, and further implementation follow from there.
Which sources exist, which are current, and which are authoritative? Where do product details, prices, or document versions conflict? Who may approve binding content? The check identifies the most important errors and risks, organizes the sources, and defines a sensible first pilot.
The pilot implements one defined area of knowledge. It includes automated ingestion, clear sources and permissions, AI answers with citations, and approvals based on risk. The result can be tested by both business and IT.
If the pilot works, further knowledge areas, sources, and use cases can follow. I work with IT leadership, architecture, and business owners to define quality criteria, responsibilities, and the operating model.
The aim is to manage knowledge reliably, from the original source to the approved answer.
Marketing, sales, product owners, and IT work with the same approved information.
New employees find current answers and their sources instead of piecing knowledge together.
Outdated versions, conflicting prices, and unclear statements become visible, reviewed, and deliberately archived.
Answers cite approved sources, show uncertainty, and can be tested systematically.
These four points guide my planning and implementation.
A plausible AI answer alone is of little use. It needs an approved source, a clear audience, and a known date or version.
Documents are not transferred into spreadsheets one by one. Ingestion, OCR, classification, version checks, fact extraction, and conflict detection are automated wherever it is sensible.
AI identifies anomalies and prepares decisions. Binding product statements, prices, and confidential content are reviewed and approved by designated knowledge owners.
A knowledge system includes sources, interfaces, permissions, approvals, archiving, and quality control. These parts need to work as one process.
25+ years in IT, including more than 20 years at Daimler and Mercedes-Benz, responsibility for business-critical systems, and hands-on work with AI knowledge platforms.
Tell me briefly which documents, systems, or departments provide different answers. In an initial conversation, we will determine whether an AI Knowledge Readiness Check is the right next step.