AI Knowledge Trainer: Practical expertise to boost your AI and automation.

In many companies, AI initiatives fail not because of the technology, but because of the quality of the knowledge base. Systems are fed with process descriptions, yet the crucial experiential knowledge gained from day-to-day practice is left out. And that accounts for no less than 80% of the knowledge that resides in people’s minds. The AI Knowledge Trainer turns human experience into fuel for intelligent systems that not only provide information but also act as AI agents.

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The challengeAI projects don’t fail because of the technology but because of a lack of understanding

Systems are often fed with dry process descriptions or historical data, yet the crucial practical knowledge gained from day-to-day experience is left out. It is precisely this knowledge, however, that determines how problems are actually solved, what exceptions arise and where the real risks lie. Without this practical dimension, the result is rigid systems that reach their limits in the real world.

of a company’s relevant knowledge resides in people’s minds – not in documents

of AI projects fail to deliver the expected value – often due to an inadequate data set (Gartner)

a higher success rate for AI projects based on validated practical knowledge

How the AI Knowledge Trainer helps

  • Operational knowledge base as a source of intelligent automation: Rather than relying on rigid theory, the AI Knowledge Trainer draws on a dynamic database of practical examples. It brings together real-world work knowledge – operational processes, difficult decisions and tried-and-tested special cases and systematically links it to existing documents and technical information.
  • Digital knowledge twins – AI learns from the best: Based on this data, the AI Knowledge Trainer uses digital knowledge twins that reflect your organisation’s operational profile. The AI does not learn from abstract rule sets, but rather from real human work patterns, how work processes actually unfold, which measures have worked in practice, and how it must operate as an autonomous AI agent.
  • From heads to scaling: The Knowledge Assistant makes implicit knowledge explicit, it captures, structures and validates it, and transforms it into scalable intelligence. Building on this, AI agents are created that can independently process complex queries and automate domain-specific processes.

Features at a glancePractical knowledge that is
actively applied

The AI Knowledge Trainer combines documents with the expertise of your specialists to train high-performance AI agents. This results in systems that actively shape processes and carry out tasks with the same confidence and precision as your best professionals.

1. Dynamic practice database

Rather than relying on rigid theory, the AI Knowledge Trainer draws on a dynamic database of real-world examples: operational processes, difficult decisions and proven special cases are systematically linked to existing documents and technical informationt.

2. Digital knowledge twins – AI learns from the best

The AI Knowledge Trainer uses digital knowledge twins that replicate your organisation’s operational profile. The AI does not learn from abstract rule sets, but rather from real human work patterns, how experts actually make decisions and act.

3. From heads to scaling

Until now, the most valuable knowledge has been locked away in the minds of experienced experts. The Knowledge Assistant makes this tacit knowledge explicit, it captures, structures and validates it, and transforms it into scalable intelligence upon which AI agents can build.

4. AI agents in action

Building on this, AI agents are being developed that can independently and comprehensively handle complex queries, automate specialist processes and respond flexibly to new requirements or process disruptions around the clock.

5. Skills shortages as a driver rather than a risk

With the AI Knowledge Trainer, valuable expertise becomes scalable for the first time. The skills shortage is transformed from a risk into a driver of digital value creation, intelligent agents ensure the quality of your best experts around the clock.

Previously without great2know

  • AI systems are trained using process documents, practical knowledge is left out
  • 85% of AI projects fail to deliver the expected value due to an inadequate data foundation (Gartner)
  • Special cases and exceptions are not documented anywhere, the system fails precisely where it is needed most
  • Valuable expert knowledge remains in people’s minds and is lost when they move on to their next job
  • Experiential knowledge is captured, structured and validated to serve as a high-quality training foundation for AI agents
  • AI learns from real human work patterns including special cases, exceptions and decision-making logic
  • AI agents handle complex enquiries independently and resolve them completely – 24/7
  • The skills shortage is shifting from a risk to a driver of digital value creation, scalable, high-quality expertise on demand