4. The Knowledge Collection
Before AI can use, understand, and reliably provide knowledge, that knowledge must first be captured completely and correctly. That is precisely why the systematic collection of company knowledge is one of the most important steps on the path to a functioning AI solution.
This is explicitly not about simply uploading documents into a database or an “AI folder.” Such storage creates files, but not usable knowledge. Documents contain content, but usually very little of the context that is crucial for professional use: In which process does this information apply? For which role? Is it up to date? What exceptions exist? What experiences have employees had with it?
An AI that only accesses uploaded files can find text passages, but it cannot truly understand what this content means in practice, when it applies, and how it must be used. An effective AI does not work like a document search, but like a knowledge-based assistant. It must recognize relationships, classify content, understand dependencies, and be able to place knowledge in a professional and organizational context.
In almost every organization, critical knowledge is located in many different places: in documents, emails, systems, presentations, training materials, tickets, logs, videos, audio files, drawings, and not least in the minds of experienced employees. A large part of this knowledge is not digital, not centrally available, or not available in a form that an AI could use meaningfully.
This is exactly where we come in. We ensure that not only files are collected, but that your company’s entire body of knowledge, in all its formats and sources, is captured and made accessible for AI in the first place. Only on this basis can an AI later emerge that does not just search, but understands, supports, and works reliably.
What happens in this step?
In this phase, the focus is still not on structuring or evaluating, but on fully capturing and securing all relevant sources of knowledge.
We collect, among other things:
- Documents, files, and data from existing systems
- Emails, logs, manuals, presentations, and training materials
- Audio and video recordings from meetings, training sessions, or interviews
- Conversations with employees that are recorded and then transcribed
- Images, scans, technical drawings, or handwritten notes
- Analog documents that are digitized
Modern methods such as speech recognition, transcription, optical character recognition (OCR), and media analysis are used. This also captures information that was previously not machine-usable, such as from videos, audio recordings, PDFs, photos, or paper documents.
The goal is to make all relevant knowledge available in digital form, regardless of the format or location in which it previously existed.
Why this step is so important
AI can only work with what is available. Missing, scattered, or non-digitized information inevitably leads to gaps, uncertainty, and incorrect answers. Complete knowledge collection ensures that nothing important is lost and that the later AI can build on the full knowledge reality of your company.
At the same time, valuable experiential knowledge is preserved:
Knowledge that previously existed only in people’s heads is retained, even when employees leave the company or retire.
What you gain from this
The systematic collection of your company knowledge ensures that your AI does not have to work with gaps, assumptions, or chance, but can build on the complete body of knowledge of your organization. This gives you the certainty that no important information is overlooked, whether from documents, systems, and media or from the experiential knowledge of your employees.
For your company, this means that knowledge is truly secured for the first time. Critical know-how remains available even when people change roles or leave. Information that was previously distributed, hidden, or difficult to find becomes centrally and digitally available. This reduces dependencies, speeds up onboarding, and prevents knowledge loss.
At the same time, transparency is created. For the first time, you can see what knowledge actually exists, where it is located, and in what form it is available. This makes gaps, redundancies, and untapped potential visible long before an AI even accesses it.
Above all, you create the prerequisite for AI to later not just search individual documents, but to use the entire knowledge space of your company. The quality of later AI responses depends directly on how complete and clean this foundation is.
19. Documentation
A professional AI system is only as good as its traceability. That is why documentation at Vimmera AI is not a by-product, but a central component of every solution. It ensures that your AI system not only works today, but also tomorrow, in a year, and in a changed organizational or legal environment. The documentation […]
18. Care, support & further development
With the go-live and the second DEX analysis, the AI has successfully arrived in your company. But just like your company itself, your AI does not stand still. Products change, processes are adjusted, new insights emerge, markets and requirements continue to develop. At the same time, the underlying technologies are also developing rapidly: new LLMs, […]
17. The second DEX analysis
After go-live begins the phase in which it is decided whether AI has not only been “introduced,” but has truly become effective. That is exactly why the DEX analysis is carried out a second time. It is not a formal conclusion, but a deliberately set measurement point to compare the state after implementation with the […]
16. Go-live
After the successful pilot phase and the joint fine-tuning, the decisive moment follows: the go-live. In this step, the AI is officially enabled for productive use across the entire defined scope. From now on, it is no longer just a pilot project, but an integral part of your working environment. The go-live is deliberately planned […]
15. Fine-tuning before the actual launch
After the AI has been introduced in initial teams or departments, a targeted phase of joint optimization, adjustment, and error correction follows, even before the system goes live company-wide. This phase is crucial for turning a functioning solution into a truly robust, practical AI. The pilot operation shows how the AI is used in the […]
14. Change Management
The introduction of AI is not just a technical project, but a profound organizational and cultural transformation. New systems, new ways of working, and new possibilities trigger uncertainty, questions, or even fears in many people. Vimmera AI knows this, and that is exactly why change management is an integral part of every AI implementation. Our […]
13. Rollout of the AI systems
Once all the technical, content-related, and organizational foundations have been established, the rollout phase begins. In this step, the AI is not simply “switched on,” but introduced into the real working environment in a controlled, supported, and coordinated manner. The goal is for the AI not only to function, but also to be understood, accepted, […]
12. Adjustment of the user interface
For AI to be effective in a company, it is not enough for it to work technically. It must be available where employees actually work. That is why Vimmera AI relies on browser-based frontends such as Vimmera Studio and Vimmera Assist. Browser-based solutions offer decisive advantages: They work on almost all devices, without local installation, […]
11. Additional data sources and interfaces
Depending on the use case, it may make sense or be necessary to supplement the knowledge base from documents and verified company knowledge with additional data sources. These include, for example, databases, business systems, and other data interfaces that can be connected via modern integration standards such as MCP. The reason is simple: not everything […]
10. Implementation of security mechanisms
In addition to selecting and combining the appropriate LLMs, another important step is implemented depending on the requirements and area of use: the implementation of additional security mechanisms. Because your knowledge is valuable, and it deserves the same protection as any other business-critical system. Vimmera AI does not view security as an option, but as […]
9. The Prompt Engineering
Once the LLM setup has been selected, the next step follows: prompt engineering. In this phase, it is determined how the AI thinks, responds, and acts. Vimmera AI handles this step in close coordination with you, because this is not about technology, but about how your AI represents your company internally and externally. An AI […]
8. Selection of the LLM or LLMs
Once knowledge has been structured, verified, and technically prepared so that it can be found and used precisely (i.e., once your company-specific Vimmera Cortex has been built), the next central step follows: the selection of the Large Language Models (LLMs). LLMs are the “thinking machines” behind the AI. They determine how language is understood, how […]
7. Data embedding and data vectorization
After knowledge has been collected, prepared, and verified, the next step follows, which ensures that the AI can later access this knowledge quickly, precisely, and with the right context: Chunking, embedding, semantic preparation, and the creation of vector databases (vector stores). This step is the technical core that ensures the AI does not search “somewhere,” […]
6. Data verification
After the company knowledge has been collected and, in the data preparation for Vimmera Cortex, structured, cleaned, and linked together, the step follows that turns information into truly reliable knowledge: data verification. In this phase, it is decided which content may actually be considered valid, binding, and actively usable. Because even technically perfectly prepared data […]
5. Data preparation
Once the company knowledge has been fully collected, the step begins that will largely determine how powerful, reliable, and useful the later AI will actually be: the data preparation. In this phase, a large number of individual files, texts, media, system extracts, and experience reports are turned for the first time into a coherent, structured, […]
4. The Knowledge Collection
Before AI can use, understand, and reliably provide knowledge, that knowledge must first be captured completely and correctly. That is precisely why the systematic collection of company knowledge is one of the most important steps on the path to a functioning AI solution. This is explicitly not about simply uploading documents into a database or […]
3. The DEX Analysis
The DEX analysis (Digital Experience & Execution) is the foundation of every successful AI implementation with Vimmera AI. It creates an objective, reliable picture of how your organization actually works. After the analysis meeting, upon request, we jointly conduct a complete DEX analysis of the processes and work steps in your company or organization. In […]
2. The process workshop
After the initial introduction and a decision in favor of AI in the company, the next crucial step follows: the analysis meeting or the process workshop. Depending on the size of the company and the complexity of the processes, either a comprehensive discussion (the analysis meeting) or a workshop with several people and, if necessary, […]
1. Getting to know each other
The use of artificial intelligence in companies does not begin with technology, but with understanding.That is why every collaboration with Vimmera AI starts with a structured introductory meeting. This meeting is intended to truly understand your organization, your challenges, and your goals. It is not a sales presentation or a technical questionnaire, but a professional […]