Question Answering Systems For Specialist Publishers

Provide specialist publications in a dialogue-based format using AI

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Specialised Publisher Q&A System

Retrieval Augmented Generation (RAG): Chatting With Your Own Data

With our question-answer systems based on Retrieval-Augmented Generation (RAG), your expert knowledge becomes more accessible than ever: Interactive AI assistants answer user questions in real time – accurately, context-sensitively, and exclusively based on your own data and content.

Whether specialist articles, technical publications, databases, or archived content – our RAG-based solutions transform existing content pools into dynamic knowledge sources. And the best part: the question-answer systems are ready to use, API-integrable, and can be customized to specific domains and topics!

“With rehm eLine Smart Assist, we deliberately harness the latest capabilities of generative AI as well as Retresco’s semantic retrieval technologies, enabling our users to access complex legal matters more easily and quickly. The great advantage lies in the combination of natural questioning, no need to sift through extensive result lists, and a concise summary answer as the outcome.”

Christine Fuß
Managing Director, HJR

Why RAG And Question Answering Systems? Because Facts Matter!

Chatbots like ChatGPT are impressive – but they often distort or invent content. For specialist publishers who rely on precision and reliability, this presents a real challenge. Our RAG-based question answering solution combines the power of generative AI with the security of your own data sources: Before generating a response, the system specifically searches your data and content pools – and formulates reliable, context-sensitive answers in natural language based on that information.

The key advantage: Our solution combines generative AI with semantic search, neural retrieval methods, and powerful parsing. This enables the system to identify even deeper semantic relationships in complex data sources – whether specialist articles or archival material.

How A RAG-Based Question Answering System Works In Practice – Using The Example Of A Specialist Publisher

A user is searching for up-to-date information to develop their B2B marketing strategy, focusing on paid newsletters and the use of AI agents. Previously, they would have had to sift through numerous magazine archives, documents, and keyword lists – with some luck, relevant articles for their specific question might have been found.

With a RAG-based question answering system, it works differently: The user asks the system their question – and receives a concrete, understandable, and factually accurate answer within seconds. The generative AI searches the internal publishing archive, existing specialist publications, and special editions, filters out relevant content, and formulates an individual response – clear, precise, and to the point. Throughout, the content quality is always ensured: only pre-checked and published materials are used.

And even better: The question-answer system not only delivers precise answers but also directly links to the relevant articles, points to related editions, and can specifically offer special publications for purchase or as subscription upgrades. This creates entirely new user experiences – personal, interactive, and conversion-driven. The RAG system becomes the digital sales assistant for content offerings!

Use Cases With RAG-Based Question Answering Systems

The use cases for RAG and our knowledge-based systems are diverse:

Automated Article Chats & Archive Searches

Users ask questions – the system delivers tailored answers from articles, archives, and databases.

Interactive News Formats

Content is prepared in a dialogue-based format and delivered individually – for greater relevance and user engagement.

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Research Assistance For Specialist Editorial Teams

RAG systems support editors in quickly finding reliable content within their own archives.

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Personalized Content Delivery

Whether full text, summary, or audio – content delivered user-centered, depending on needs and usage context.

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Content Aggregation & Repurposing

Relevant content is automatically combined and curated – for repurposing across various distribution channels.

Sonja Hassler, Head of Digital Products, Walhalla Media Group

"Our goal is to make access to relevant legal information as easy as possible for professionals in the public sector, administration, the armed forces, and social services. To achieve this, we developed KIRK – our AI-supported legal research tool – using Retresco’s RAG solution: Instead of tediously sifting through laws, commentaries, and rulings, users receive a structured and understandable answer quickly, even for complex specialist queries – directly from the relevant works, with all important references and sources. This saves valuable time, provides confidence in case handling, and ensures efficient workflows."

Question Answering Systems With RAG: Making Expert Knowledge Efficiently Accessible

With our RAG-based AI applications, you make your expert knowledge accessible on demand:

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Fast Setup & Easy Integration

Our question answering systems are ready to use in no time – without lengthy IT processes. TThey are tailored to specific use cases in a flexible and scalable way, according to the needs of your editorial team or specialist department.

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Simple Data Integration

Internal and external data sources such as XML, JSON, or PDFs can be easily integrated via standard API. Content can be automatically structured by topics, authors, or publication dates – completely according to your editorial setup.

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Automated Source Referencing

Every answer transparently references the underlying content and data sources. This builds trust – especially with complex or fact-based specialist content.

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Real-Time Answers

Questions are processed dynamically, and relevant answers are delivered immediately. This creates interactive user experiences – directly from your own data repository.

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User Feedback Included

An integrated feedback module allows users to rate the quality of answers. This way, question answering systems can be continuously improved—data-driven and tailored to the target audience.

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Flexible Frontend Design

The interface is easily customizable to match corporate identity, colors, and design guidelines. This ensures a brand-consistent, intuitive, and high-quality user experience – from the first click to the final answer.

Do you want to know how a RAG-based question answering system can advance the delivery and monetization of your specialist articles, databases, and archival content?

Get in touch with us – we’ll be happy to show you concrete use cases!

Why choose a question answering system from Retresco?

Retresco’s RAG-based system
ChatGPT & comparable systems
Architecture Dynamic & modular: real-time adaptation to query context through integrated retrieval and generation components Retrieval processes
Retrieval processes Intelligent selection of suitable sources, formats & answer types depending on the question context Vordefinierte Suchlogiken, eingeschränkte Anpassbarkeit an unterschiedliche Fragestellungen
Data integration Seamless integration with CMS, archives, content pools, and internal knowledge systems Limited connectivity options to editorial systems and internal data sources
User interaction Interactive question model with feedback loops for continuous optimisation and personalisation One-dimensional question-answer dialogue without feedback integration or learning mechanism
Context understanding Deep understanding through semantic search, source verification, and multi-level inference Limited context understanding, based on general training data
Content quality High reliability through verified content, source referencing, and editorial origin Susceptibility to errors due to external, non-curated data and lack of source transparency
Personalisation Tailored for specialised publishers: topic-, domain-, and industry-specific configuration Hardly customisable, usually trained on generic content and use cases
Automatisierung Automated content delivery and prioritisation according to editorial and strategic guidelines Simple answer output without editorial control or content prioritisation
Scalability Optimised for large content volumes and versatile output formats Limited processing capacity with complex requirements or large data volumes
Analysis & insights Detailed analyses and KPIs on usage, performance, and improvement potential Minimal insights into user behaviour or answer quality
Intuitive user interface Fully customisable: design, branding, and user guidance can be integrated Standard interface with limited design flexibility
LLM integration Fully flexible: Integration of any desired language model (open source or proprietary) possible Tied to the provider’s specific pre-installed model
Languages / localisation Multilingual output with country-specific SEO and linguistic fine-tuning Mostly English-centred, with translations lacking cultural or technical nuances
Updates Continuous development with a focus on industry-specific features Standard updates, independent of industry or customer feedback
Support Personal support from German-speaking AI and industry experts Generic online support without editorial or domain-specific expertise
   

Would you like to learn more about RAG applications and question answering systems?

Contact us – we are happy to show you concrete use cases

Discover new solutions for specialised publishers and special-interest offerings!

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Your contact person

Aleksandar Petrovic