Retrieval Augmented Generation. How to get trustworthy answers from your documents

RAG (Retrieval Augmented Generation) assistants developed by Kairntech enable business and AI users to make the most of their documents. Thanks to an easy-to-use solution and extensive customization options, RAG experiments are quickly industrialized in a secure and scalable manner.

Can Kairntech’s AI create business impact from your documents?

RAG (Retrieval Augmented Generation) assistants

There is no one-size-fits-all RAG solution. Every use-case is different. Data sources are different, the way you ask questions is different and the expected reply formats may be different as well.

Some examples of situations where RAG can enhance your documents and deliver business impact:

For scientific literature analysis the build-in connectivity to sources (PubMed…) and support of scientific articles conversion & parsing enables targeted search and question answering with metadata and filters.
For internal audit or M&A datarooms RAG is perfectly suited for information discovery alongside the automatic creation of check lists.
For support or training it is often helpful to pre-load relevant questions.
For compliance the text contained in complex excel spreadsheets can be leveraged. Ask regulatory questions to speed up compliance process.
For competitive intelligence (OSINT sources) an initial effort is needed to identify the sources but most importantly the scope of what you want to monitor. Document classification and Named Entity Recognition (NER) are useful tools to improve the quality of question answering.

Quickly prototype a RAG assistant

50+ pre-packaged technical components and models including document chunking, embeddings, proprietary and open source LLMs…
A wide range of technologies throughout the RAG value chain.
Accessible to analysts and business users

Enhance the quality of RAG assistants

Obtain the highest accuracy with an extensive range of customization options
Benefit from our capacity to deliver value with highly complex use-cases
Improve RAG quality by comparing search methodology, vectorizers, LLMs and question-answer benchmarking
View source documents to check the context

Industrialize RAG assistants securely

Embed RAG pipelines easily through a rich REST API.
Apply single sign-on (SSO) and customize the user interface to fit corporate branding requirements.
Connect content and document management systems while respecting existing access rights
Scale within a distributed environment and deploy safely on premise if required.

How does Kairntech RAG work?

Prototype quickly

Uploaded documents are indexed, segmented and vectorized automatically. Start asking questions straight away!

Customize extensively

Experiment with search methods, embedding models, LLM prompts, document metadata or annotations from custom-build AI models. Find out more in our whitepaper.

Deploy seamlessly

Deploy customized RAG projects to different business groups. Either embedded within an existing application, or using a simplified and customized Kairntech user interface.

Retrieval Augmented Generation Questions & Answers

Only relevant text chunks are shared with LLMs, not the full documents. In depends on the agreement with whether LLM providers are allowed to train their models based on the text chunks that are uploaded.

Yes, Kairntech provides alternatives to proprietary LLMs (OpenAI…) with open source ones (Llama, Mistral…)

Yes, we implemented the RAG (Retrieval-Augmented Generation) framework to build question answering assistants. This approach integrates the power of information retrieval (semantic search) with LLM text generation.

Yes, this is possible by fine-tuning the contextual embedding models on a specific business domain. We need a representative corpus of the content in order to train the model (subject to professional services).

Yes, it is possible to have a customized support. Kairntech can develop specific solutions according to your specifications in a few days.

100% secure, 100% transparent

All our data storage systems take into account the constraints of the GDPR.

Manage fine-grained access rights to facilitate access to multiple stakeholders.

In the cloud or on-premise, choose the mode that best suits your organization.

Can Kairntech help to build your own RAG assistant?

They trust us

iQNow platform integrates AI via Kairntech for Boehringer scientists, facilitating access to vast information, enabling natural language queries, and refining results through experimentation.
Dr. Michael Schorpp, Head of Knowledge Management and AI at Boehringer Ingelheim

To make the most of our content, it is essential to build high-quality AI models simply and quickly and to combine them with knowledge base.
Gaëlle Recource – Head of Content – Groupe Revue Fiduciaire