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,” but instead specifically finds the right knowledge, even when users use different words, abbreviations, or ask incompletely. It largely determines whether answers are stable, reproducible, and technically sound.
What happens in this step?
In this step, the verified knowledge base is transformed into a form that an AI can search efficiently and use reliably.
First, content is broken down into smaller, thematically coherent knowledge units. This process is called chunking. The goal is not simply to split texts into paragraphs, but to cut technically related content in such a way that each piece remains understandable on its own while still retaining context. Technical instructions, processes, product information, or rules each require different structures. Overlaps between chunks ensure that important connections are not lost.
Each of these knowledge building blocks is then assigned metadata. These are context details such as product, service, category, process, department, target group, region, version, validity, or approval status. This metadata is crucial so that later not just “similar text,” but the right content in the right technical context is found.
Next, these knowledge building blocks are vectorized. The text is translated into a mathematical representation that maps its meaning. This allows the system later to search not only for words, but for meaning. A question about “price” can thus also find content about terms and conditions, discount logic, quotation rules, or list prices, even if these terms are not phrased identically.
These vectors are stored together with their metadata in a vector database, a so-called vector store. These systems are specialized in quickly finding the most relevant knowledge building blocks from very large amounts of data. Additional mechanisms are used, such as filters by product, region, or approval status, prioritizations, combinations of semantic and classical search, and re-ranking of the best results.
In addition, it is defined how the system may search later: how many knowledge building blocks are used per query, which sources have priority, how conflicting information is handled, and which filters are applied automatically. This creates controlled, reproducible access to knowledge.
Why this step is so important
One of the biggest weaknesses of many AI systems is that answer quality declines as the amount of data grows. The more content is considered at once, the greater the ambiguity becomes. Information gets mixed together, relevant details are lost, and answers become more general or inconsistent.
Chunking, metadata, and vector search fundamentally solve this problem. The AI no longer accesses a large mass of text, but exactly the knowledge building blocks that are technically relevant to the respective query. This keeps quality stable, even with very large knowledge bases.
This step is also the foundation for reproducibility, security, and scalability. The same questions access the same knowledge building blocks, approvals and roles can be taken into account, and even complex questions spanning multiple subject areas can be handled cleanly.
What you gain from this
Through embeddings and vector databases, your AI gains the ability to use knowledge in the way it is actually needed in everyday work. Employees can ask in their own language, using abbreviations, technical terms, or company-specific phrasing, and the system still finds the right content.
You get more precise, more consistent answers, even with very large amounts of data. Knowledge is no longer selected vaguely or randomly, but in a targeted and traceable way.
At the same time, you gain control and security. You can determine which content is visible, which versions apply, and which contexts are taken into account. And you create the technical foundation for AI to later do more than just provide information: it can reliably support complex tasks because it correctly combines knowledge from different areas.
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 […]