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 texts are generated, how logically arguments are made, and how flexibly the system can respond.
Vimmera AI does not select these models in a blanket way, but together with you and based on your specific requirements. Because there is no single best model. There are large and small models, very creative and very precise models, fast and resource-efficient models, as well as highly specialized models for specific tasks. Depending on the use case, a single model may make sense, or an interplay of several specialized models.
What happens in this step?
In this step, it is determined which LLMs are used for which tasks. Together, it is decided which capabilities are needed: for example language quality, subject-matter expertise, computing power, speed, data protection, offline capability, or cost control.
Highly capable online models can be used, for example from providers such as OpenAI, Google, or Meta. Offline models can also be used, running on your own servers, in private cloud environments, or even locally on individual computers, such as models from OpenAI, Deepseek, Anthropic, or other providers. The choice depends on which security requirements, data protection regulations, performance goals, or budget constraints apply to your company.
Vimmera AI is not tied to individual manufacturers. All common and powerful systems can be integrated, combined, and orchestrated. This creates AI architectures that fit your organization exactly instead of forcing your organization to adapt to an AI.
Multiple models, one system
In many projects, not just a single LLM is used, but several specialized models. One model may be responsible for the actual subject-matter answers, another for preprocessing inputs, for example to anonymize sensitive data, filter unwanted content, or increase security. Additional models can be used for quality control, structuring outputs, or summarization and further processing.
These models are linked together and orchestrated so that they work as a shared system. For users, only a powerful, consistent AI assistant is visible; in the background, however, several specialized AI instances work together to maximize security, quality, and subject-matter expertise.
How much “knowledge” may the model contribute itself?
A particularly important point in this step is deciding what role the general world knowledge of the LLMs may play. Modern language models bring enormous prior knowledge from their training phase. This knowledge can be helpful, for example for general contexts, language, or logical inferences. In some scenarios, however, it is undesirable because only verified, validated company knowledge may be used.
Together with you, it is therefore determined whether a model may contribute its own knowledge or whether it is deliberately used as an “empty shell” that accesses almost exclusively your company data. This ensures that answers are not based on external, possibly incorrect or unapproved knowledge, but on exactly what your company specifies.
What you gain from this
Through the targeted selection and combination of LLMs, you do not get a standard AI system, but a tailor-made AI architecture. You receive exactly the mix of performance, security, cost control, and subject-matter expertise that fits your requirements.
You retain control over where your data is processed, which models are used, and how strongly external systems are integrated. At the same time, you benefit from state-of-the-art AI technology that can be flexibly expanded, replaced, or adapted as requirements change.
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 […]