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 centralised backend that brings together scattered organisational knowledge in a structured way and makes it reliably controllable for AI applications. Find out in this concise 5-page document 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.

Download

white paper: TYPO3 development, SEO strategy and accessibility
This white paper explores the interplay between a high-performance enterprise CMS, data-driven visibility and digital inclusion for sophisticated web portals. Discover in this concise 9-page document, using practical examples from the institutional sector, how to combine technical excellence with measurable success:  

• Scalable TYPO3 expertise: bespoke 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 rectification and ranking optimisation.  
• Enterprise SEO in practice: Detailed analyses of the success factors (local SEO, E-E-A-T guidelines, multi-regional architecture) of 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. 

Download

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 this 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 the length of vectors without any significant loss of accuracy, thereby saving a huge amount 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.

Download

White paper: The evolution towards 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 to knowledge graphs provides the LLM with a logically validated knowledge network instead of isolated blocks of text. Essential for cross-document queries.
• The fourth dimension (temporal graphs): Time attributes (valid from/to) contextualise data historically. Queries are answered with precision to the exact reference date and are audit-proof; obsolete data becomes invisible to the AI.
• Architecture & XAI: Implementation is via a multi-model database (PostgreSQL) or a 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.

Download

Beyond Plain Text: Why standard RAG fails when it comes to your spreadsheets and invoices – and what the solution looks like.

Anyone seeking to make internal documents accessible via an AI chatbot quickly hits an invisible wall: standard RAG systems regularly falter when faced with complex spreadsheets, financial reports and structured invoices. Why? Because mathematical vectors transform two-dimensional layouts into a one-dimensional wasteland of text – resulting in hallucinations and data loss.

In our latest white paper, we take a look under the bonnet of Advanced Multimodal RAG. Find out how, at rms., we use a powerful pipeline comprising Kreuzberg-PDF, Tesseract and PaddleOCR to precisely isolate visual structures, how ChromaDB and metadata filtering guarantee error-free financial data, and why state-of-the-art rerankers from Jina or IONOS are the crucial piece of the puzzle for production-ready AI systems in enterprise environments.

Download

How to give your enterprise AI search a bespoke brand personality

Standard language models usually respond politely, neutrally – and in a way that’s entirely interchangeable. For companies that prioritise a clear brand identity and a tone tailored to their target audience, this standardisation quickly becomes a source of friction. But how do you teach an enterprise search system to sound exactly like your own company without sacrificing technical precision?

In this practical white paper, Michael Kettel (rms) explains why a simple sentence in the system prompt is not enough. Using a concrete architectural blueprint, discover how the interplay of persona definition, RAG metadata, technical guardrails and UX creates a consistent digital brand ambassador.

What you can expect from the white paper:

• The 4 pillars of AI personality: from role-based behavioural models to few-shot prompting.
• Corporate language in RAG: Why the structure of the vector database determines the tone.
• Security before style: How context-dependent fallbacks safeguard legal and critical enquiries.
• B2B practical case study & test catalogue: A step-by-step approach for enterprise portals (e.g. based on TYPO3), including evaluative criteria that can be automated.

Format: PDF | Length: 5 pages | Target audience: IT managers, digital leads, marketing & brand managers

Download