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, and professionally robust knowledge system.
This is where Vimmera Cortex comes into play. Our knowledge base with a very special data structure.
The collected information initially exists in very different qualities. Often there are multiple versions of the same content, contradictory statements, outdated versions, informal working methods, inconsistent terminology, or missing links between related topics. Much of it has grown historically, is not maintained consistently, or is designed for different target groups and situations.
A particularly important part of data preparation is the standardization and structuring of your company’s language. Technical terms, internal designations, product names, abbreviations, process names, and typical formulations are prepared and linked in such a way that the AI understands and correctly uses the “language” of your organization, your employees, and your customers. Only then can requests be interpreted correctly, content assigned unambiguously, and misunderstandings avoided. Without this linguistic clarification, many questions lead nowhere or are answered inaccurately, incorrectly, or incompletely.
If an AI were to work directly on the unstructured raw material (that is, in simple files and documents), it could find text passages and content, but it could not reliably decide which information is valid, current, and technically correct. It becomes even more difficult with complex questions in which several areas of knowledge, rules, products, or processes interact. Without preparation, the AI lacks the necessary clarity to recognize relationships with confidence, argue without contradictions, or provide complete, reliable answers.
Data preparation is therefore the step in which a solid knowledge base emerges from unstructured information. A knowledge base that is technically consistent, speaks your company’s language, and creates the prerequisite for AI not only to find, but to understand, evaluate, and provide meaningful support.
From document to knowledge
Another, often underestimated aspect is the quality of later answers when dealing with large volumes of data. The more information is placed into a (vector) database and the more data an AI has to consider at the same time, the greater the fuzziness of the matches and outputs becomes. This is a well-known problem in many systems:
As the amount of data grows, precision decreases, answers become more general, less accurate, or mix content that is technically unrelated.
The reason is simple: in classic systems, thousands of pages, PDFs, protocols, or manuals sit side by side. For the AI, these are equivalent text sources. Relevance, validity, responsibility, product reference, or technical context are not clearly represented in them.
The data preparation for Vimmera Cortex solves this problem fundamentally by treating information not as documents, but as knowledge. Content is broken down into its technical components: into facts, rules, terms, questions, answers, relationships, dependencies, validity periods, variants, and links. The individual document loses its role as a knowledge container; it is “dissolved.” What remains is the knowledge it contains in structured, abstract form.
The best way to imagine this process and storage is how we humans store memories. After all, we don’t store a PDF of an assembly manual in our brain when we want to remember how our television works.
The previously collected knowledge is clearly assigned to products, services, processes, functions, categories, or application scenarios. As a result, the total amount of data no longer matters. The AI does not access a large, fuzzy mass of text, but exactly the knowledge building blocks that are relevant to the respective request.
If desired, the original documents remain available. You can still look up where something is written, find documents in a targeted way, or search text passages.
What matters, however, is:
The AI from Vimmera AI does not work with documents – it works with knowledge from Vimmera Cortex, that is, with your knowledge.
Linking instead of filing
In data preparation, content is not only cleaned up and standardized, but above all linked with one another. Different sources of knowledge, such as documents, processes, products, rules, experience-based knowledge, and system data, are put into relation. This creates a knowledge network in which the AI knows not only individual facts, but also their meaning, validity, and context.
Among other things, it is defined:
- which documents belong to which processes
- which products, item numbers, variants, and rules belong together
- which functions are intended for what and when they are not useful
- which exceptions, alternatives, or dependencies exist
- which information should be considered together in which situations
Only then can the AI later not only find information, but also classify, combine, and evaluate it correctly.
Technical logic instead of chance
In the data preparation for Vimmera Cortex, it is also defined which links should be applied automatically in which situations. This creates a technical logic according to which the AI works.
Examples:
- For a price inquiry, the system automatically recognizes the product, the matching item number, associated variants, valid discount groups, and relevant conditions.
- For a question about a function, it not only explains what it does, but also whether it is useful in this specific situation, which limitations apply, or which alternatives would be better suited.
- For service requests, the device, error code, known causes, suitable spare parts, and proven solution steps are automatically linked with one another.
- For process questions, responsibilities, forms, guidelines, and dependencies are considered simultaneously.
Such answers are only possible when knowledge has been structured, interconnected, and technically modeled.
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 […]