Retrieval Augmented Generation (RAG)

Combine GenAI with your company knowledge

Large generative AI language models have huge potential and have long since arrived in our everyday (working) lives. But what about aspects such as trust, accuracy and explainability? The RAG approach addresses these and makes the use of large language models safer.

We develop your generative AI solution with RAG functionality, from conception and data connection through to implementation and secure operation.

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We develop your individual RAG solution

RAG combines the skills that an LLM has acquired during their training with domain-specific knowledge. The generally available training data from the Internet is therefore enhanced with your information. Data sheets and operating instructions for your products or your internal wiki: The RAG approach takes your knowledge management to the next level, simplifies solution finding in a wide variety of use cases and thus increases productivity.

On our data analysis blog, we explain more about how the RAG approach works and show you examples of its use in your company.

How you benefit from your individual RAG solution

Possibility to generate reliable answers to your specific questions and problems, which would not be possible without RAG, as relevant information is not available for model training

Better, more reliable and more up-to-date results through the secure integration of your company knowledge and the specialization of the LLM to your specific use case or industry

Reduction of the risk of hallucinations (generation of fictitious / inaccurate information) or false information

Full flexibility in the choice of RAG pipeline components (LLM, embedding models, database) for maximum performance and independence from third parties

Seamless integration into your existing applications and customizable user interfaces

With us to your RAG solution

We accompany you from the idea through implementation to operation

Evaluation and design of the right use case

In which area do you see potential for the use of RAG? We work with you to identify the most promising use cases, evaluate them for business value and feasibility and develop the concept for successful implementation.

Selection and customization of the LLM

We keep the overview for you: From a wide range of available language models with different strengths and weaknesses, we select the optimum model for your application and adapt it to your individual requirements - for optimum results.

Data management and data connection

RAG's recipe for success lies in the integration of your corporate knowledge. To do this, we work with you to identify and prioritize the relevant data sources, prepare them and securely link them to the selected language model.

Frontend development and integration

Where and who should your RAG solution support? Your customers on your website when answering service requests or your employees with internal knowledge management in your wiki system? We develop the appropriate user interface or integrate it directly into existing tools.

Operation and support

Our infrastructure experts create the technical framework for the secure and reliable operation of your RAG solution. On-premise, in your existing cloud solution or via us - you have the choice of where and how your RAG solution should be operated. Of course, we also support you as a permanent contact partner.

Certified security for the development of your GenAI solutions

As a customer, you have the highest demands in terms of availability and security - especially in a sensitive data environment. We are aware of this, which is why we as a company and as part of VIVAVIS AG, which specializes in critical infrastructures, stand for maximum confidentiality and integrity and our solutions for maximum security - certified in accordance with ISO 27001.

A selection of our references

Retrieval Augmented Generation Referenzkunden eoda

Case study: Optimizing knowledge management and increasing productivity with generative AI

Find out how we were able to implement a RAG solution at a medium-sized mechanical engineering company, how quickly it was accepted by employees and how it helps them find relevant and precise information faster every day.

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