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 is not yet a solid foundation without expert review. In every organization, official rules, established practice, individual experience, old documents, and new guidelines exist in parallel. These often contradict one another or apply only in certain contexts. If an AI were to treat all of this information equally without filtering, it would inevitably produce incorrect, outdated, or contradictory statements.
Data verification ensures that the knowledge base does not become merely a collection of opinions, but rather a clear, professionally aligned, and accountable knowledge foundation that your company can also stand behind externally.
What happens in data verification?
In this step, the previously prepared knowledge assets are specifically checked, evaluated, and approved. Subject-matter experts, defined roles, or relevant committees decide which content officially applies, which versions are authoritative, which rules, processes, product information, or statements are binding, and where exceptions or restrictions exist.
It is also determined which content is informative or historically relevant, but must not be actively used by the AI as a valid answer. Only after this deliberate approval are contents transferred into the operational knowledge base that the AI later accesses.
Vimmera AI provides intensive support for this process. Our experienced colleagues support your employees professionally and methodically, provide suitable tools, and ensure that verification runs efficiently and in a way that works in day-to-day operations. This process can even be integrated directly into the use of the AI: for example, the AI can specifically ask whether information is correct or whether someone has a moment to confirm or correct a statement.
During this review, content can not only be confirmed, but also supplemented, clarified, or linked with additional references. In this way, the quality of the knowledge base grows continuously without losing control.
Why this step is so important
Data verification is the point at which technology becomes responsibility. It ensures that the AI does not just say anything, but exactly what your company wants to stand behind professionally, legally, and organizationally.
This keeps it clear at all times which statements are binding, which are recommendations, and where room for discretion is intentionally left. Especially in sensitive areas such as engineering, service, sales, quality assurance, legal, or HR, this clarity is crucial. Only in this way does an AI emerge that employees can trust, whose statements are reliable, and that represents your company safely both internally and externally.
What you gain from it
Data verification gives you the assurance that your AI does not just know a lot, but knows the right things. It ensures that all answers, recommendations, and analyses are based on a professionally reviewed, aligned, and accountable level of knowledge.
For your company, this means that the AI’s statements are reliable, regardless of who uses it. Employees receive consistent, uniform information instead of contradictory answers. Customers and partners experience a professional, clear appearance. Risks from outdated documents, informal special solutions, or incorrectly interpreted rules are significantly reduced.
At the same time, you retain control at all times over what your AI may say and what it may not. You decide which content is binding, where recommendations may be made, and where clear boundaries are intentionally drawn. This keeps professional and legal responsibility where it belongs: with your company.
In addition, data verification creates a permanently high quality of your knowledge base. New knowledge can be added, checked, and approved in a controlled manner without jeopardizing existing reliability. As a result, your AI remains up to date, capable of learning, and stable at the same time.
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 […]