Digital Relevance is Key – for Humans and AI | Digital Business Relevance Suite (DBRS) 

Relevance = visible content ∧ context ∧ intelligent processing by humans and AI

• Visible Content
  Expertise, data, experience, and documents—available both internally and externally.
  
  
• Context
  Classification according to objective, situation, role, timing, and question.
  
  
• Intelligente Processing
  Through people and AI: understandable, comprehensible, verifiable, and effective.

If one of these components is missing, there is no relevance:

• Content without context remains meaningless.
• Context without content remains empty.
• Intelligent processing without both leads to incorrect or random results.

DBRS focuses precisely on this logical AND connection.

The Digital Business Relevance Suite boosts your visibility and relevance both externally and internally.

• We deliberately recommend smaller, clearly defined projects that allow participants to experience AI innovation in a safe and positive way.
• The project method Experience Innovation project method (described below) ensures that AI, people, and processes interact reliably and successfully.​

Harvard Business Review Study: Overcoming the Organizational Barriers to AI Adoption.

Many people use AI portals in an unstructured creative mode: they try things out but document little. This means that the practical benefits remain unclear and learning is random—a recent Harvard study refers to an “invisible wall” preventing further benefits.

Vibe Mode (structured creative mode)

Structured experimentation: testing ideas, trying out learning content, recognizing patterns, building initial solutions and prototypes.

The end result is understandable, comprehensible, and verifiable results, ideally a simple prototype with a brief description—the basis for the next step.

CAISE Mode (Engineering-Modus)

This is where the quality-oriented, documented development process begins with a clear goal.

AI acts as a supportive team member, decisions and results are validated:

That's what we call Collaborative AI Supported Engineering (CAISE).

An example of how the technical DBRS page is integrated into a website can be found at https://tolksdorf.digital/dbrs-llm-knowledge-hub/ . The page is purely an informational landing page, deliberately kept simple to facilitate the work of LLM/AI.

How „thinks“ a LLM?

A large language model (LLM) does not think. It calculates the most likely answer based on data, context, and statistics. Very useful—but not infallible.

Why is Artificial Intelligence (AI) intelligent?

An LLM, also known as AI, is excellent at handling language and therefore appears intelligent, even empathetic, to users.

How think people?

Human thinking is embodied, emotional, social, and culturally influenced. We combine experience, perception, and emotion—far more than mere information processing.

The strength lies in the connection (Human in the Loop):

People contribute experience, goal orientation, and judgment—LLMs provide speed, structure, and ideas.

Together, they form a powerful duo: human-led, AI-supported, and significantly better than either side alone.

The answer to this question varies depending on one's disposition. It is an astonishingly feasible innovation step that can bring enormous benefits to companies of all sizes, or it can be detrimental. It should not be ignored. ​

• As with any tool, its everyday use determines how useful it is. A tool is useless if it just sits in a box.

• Greater effectiveness: Initially, the gains in skills and opportunities outweigh the losses.
• Greater efficiency: Once new skills have been acquired, learned, and tested, the efficiency gains outweigh the costs in recurring applications.

• Administrator access to the website must be possible; alternatively, close cooperation with those responsible is necessary.

• Data quality and distribution in data silos: Our suite helps to prepare and utilize this data.
• Acceptance within the team: AI tools such as Samy are new – we offer training courses to help employees feel confident using the technology.
• If search engine optimization (SEO) has not yet been carried out on the website, this topic is added. AI is used to reduce the amount of work involved.

• Every company has its own starting point, which leads to different workflows and levels of effort.

• Context Engineering: Data processing. AI is used to reduce effort, and results are checked by humans.
• Prompt engineering for AI used in workflows.
• Workflow and integration engineering for planned workflows.
• There are templates for the workflows, but customization and testing are still required.
• Experience value for the effort: a few hours to a few days per workflow.

• This is determined in the Relevance Radar Workshop and the Quick Checks.
• chatGPT & Co find you and formulate answers that suit you instead of guessing or ignoring you.

• Truly intelligent interactive chatbots improve customer loyalty and surprise users in a positive way.
• Improvement of data quality for further processes.
• Increasing digital maturity and innovative strength.

• Yes, for internal processes.

• Yes, the Digital Business Relevance Suite complements your website. Code snippets invisible to users are added and robots.txt and sitemap.xml are supplemented.

• SEO = Search Engine Optimization, i.e. optimization for traditional search engines
• GEO = Generative Engine Optimization, i.e., optimization for AI such as Google Gemini, chatGPT, Claudem Mistral, and many more.
• SEO and GEO are equally important these days
• Further information can be found at  https://tolksdorf.digital/kmu-opensource-ki-hub

• Security is not only about where the AI is operated (“hosted”) but also how it is integrated. In this solution, all user inputs in the chat client are anonymized, which allows for a wide range of AI options on this side. In each case, it must be checked whether European solutions such as the open source LLM Mistral are preferable for compliance reasons.
• Sustainable security also includes the freedom to choose between different LLM providers. This allows future or changed requirements to be taken into account.

DBRS enables the orchestration of website information in accordance with AI Overviews (Google Search), Helpful Content / E-E-A-T, formerly Search Generative Experience (SGE).

Optimization for search engines and AI systems (often referred to as GEO – Generative Engine Optimization) requires a combination of classic SEO methods and new, dialogue-oriented strategies.

• Combined optimization:
  Visibility is improved by combining classic SEO methods with dialogue-oriented GEO strategies.
• Answer-First-Prinzip:
  Key answers are placed right at the beginning of a paragraph or article, as AI systems specifically search for concise answers.
• Structured Data:
  Content is marked up with Schema.org-Markups (JSON-LD) to enable search engines and AI systems to interpret it unambiguously.
• Quastions and Answers (FAQs):
  Content is structured in clear question-and-answer formats, with W questions used as headings, as these are often extracted by AI.
• Experience, Expertise, Authoritativeness and Trustworthiness (E-E-A-T):
  Credibility is enhanced by author profiles, source references, study links, and mentions on established third-party platforms.
• Conversational content:
  Texts are written in natural, conversational language, as AI-supported search systems prefer to cite such content.
• Technical accessibility:
  Access for AI crawlers is enabled (e.g., via robots.txt), and relevant content is provided directly in HTML.
• Up-to-date and fact-based:
  Content is updated regularly, as AI systems give preference to current data and statistics.