Welcome to our white paper section. As the rms agency, we not only support you with operational matters, but also share our strategic expertise with you. Here you will find in-depth expert knowledge, up-to-date industry analyses, best practices and practical guides covering content management systems, AI and knowledge databases.

Our white papers are designed to provide you with real added value and immediately actionable insights for your day-to-day work.

White paper: Consolidating knowledge, controlling AI – the rms. AI Hub
This white paper introduces the rms. AI Hub – a central backend that brings together scattered corporate knowledge in a structured way and makes it reliably controllable for AI applications. Find out in this concise 5-page guide how you can drastically reduce search times whilst retaining full data sovereignty:  

• Precise answers (RAG): Fact-based results in milliseconds via an integrated vector database (Chroma DB).  
• Comprehensive integration: Seamless connection to SharePoint, Confluence, Slack and scanned documents via an OCR pipeline.  
• Specialised AI agents: Automated, cross-system analyses for research, compliance and trends.  
• Maximum security: Fully GDPR-compliant operation and granular role models within your infrastructure.

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White Paper: TYPO3 Development, SEO Strategy and Accessibility
This white paper explores the interplay between a powerful enterprise CMS, data-driven visibility and digital inclusion for sophisticated web portals. Discover in a concise 9-page guide, using practical examples from the institutional sector, how to combine technical excellence with measurable success:  

• Scalable TYPO3 expertise: Tailor-made back-ends, complex interface integrations and innovative features such as the AI-powered 
• RMS AI SEO Extension. Strategic on-page & technical SEO: Quality assurance and monitoring using professional tools such as Seobility for proactive error resolution and ranking optimisation.  
• Enterprise SEO in practice: Detailed analyses of success factors (local SEO, E-E-A-T guidelines, multi-regional architecture) from high-profile reference projects such as EVIM, drw.de and ib.de. 
• Accessibility in practice (WCAG & BFSG): Technical implementation of digital inclusion in the TYPO3 system through semantic HTML, keyboard navigability and front-end tools such as Eye-Able. 

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White Paper: From Vector Space to Multimodal Search – Embedding LLMs as the Foundation of AI
This white paper highlights the key technological role of embedding models as a crucial link for precise knowledge retrieval (RAG) in modern enterprise AI architectures. Discover in a concise 5-page guide how to translate unstructured data into a mathematical map and overcome the limitations of pure text search:  

• Semantic vector spaces: Why traditional keyword searches systematically fail and how embedding models translate the human ability to abstract meaning into efficient mathematics.  
• Comparing architecture classes: A strategic comparison of proprietary APIs and locally hosted open-source models in terms of latency, data privacy and operating costs.
• Matryoshka Representation Learning (MRL): How modern training methods drastically reduce vectors without any significant loss of accuracy, thereby saving massive amounts of storage space and computing time. The new generation (Qwen3-VL-Embedding-8B): How native vision-language models ‘see’ complex PDF layouts, diagrams and balance sheets directly, rendering error-prone OCR intermediate steps obsolete.

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White paper: Evolution to GraphRAG and temporal context graphs

• The problem: Traditional RAG (pure vector search) fails in enterprise use due to loss of context, the confusion of logical opposites (cosine trap) and hallucinations.
• The solution (GraphRAG): Linking vectors with knowledge graphs provides the LLM with a logically validated knowledge network instead of isolated text blocks. Essential for cross-document queries.
• The fourth dimension (temporal graphs): Time attributes (valid from/to) contextualise data historically. Queries are answered with precise reference dates and are audit-proof; obsolete data becomes invisible to the AI.
• Architecture & XAI: Implementation is via a multi-model database (PostgreSQL) or hybrid stack (Neo4j). Visual front-ends (Semantic Zoom, Time-Travel UI) make thought processes and history transparent to specialist departments.
• Conclusion: Critical hallucinations are not eliminated by larger language models, but by a superior, time-sensitive data architecture.

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