RAG Chatbot solution

The RAG (Retrieval Augmented Generation) Chatbot solution enables business and AI users to get the most out of their documents by building tailor-made chatbots. Thanks to this easy-to-use solution and extensive customization options, RAG chatbots are quickly industrialized in a secure and scalable manner.

Can Kairntech’s AI create chatbots from your documents?

Chat with your data

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  • Use multi-turn conversation & history
  • Gain confidence through accurate, well-sourced answers
  • Adjust output style & tone

Configuring your chatbot in a user interface

  • Adjust query rephrasing & transformation
  • Optimize the retriever
  • Leverage metadata & knowledge
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Monitor & maintain your Chatbot

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  • Track user interactions and response quality
  • Handle errors like irrelevant retrievals, poor responses…
  • Update LLMs, knowledge base and retrain models

How does Kairntech RAG Chatbot work ?

1


Prototype quickly

Uploaded documents are indexed, segmented and vectorized automatically.

Start asking questions straight away in the chatbot!

2


Customize extensively

Experiment with search methods, embedding models, retrievers, LLM prompts, document metadata and much more!

3


Monitor & Maintain

Deploy chatbots to different business groups. Either embedded within an existing application, or using a Kairntech chat user interface.

100% secured, 100% transparent

GDPR

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

Access

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

Hosting

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

They trust us

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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 Boerhinger Ingelheim
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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

Can Kairntech help to build your own RAG Chatbot solution?

We are here to help

The data sources for the retrieval component should be domain-specific, authoritative, and comprehensive. Examples include internal knowledge bases, industry-specific documents, research papers, or curated public datasets. To ensure quality and relevance we:
– Evaluate the retrieval performance using metrics like precision, recall, and F1-score on a validation set.
– Perform data cleaning to remove noise, duplicates, and irrelevant content.
– Use domain-specific ontologies or taxonomies to structure the data.
– Regularly update the data sources to reflect the latest information.

Fine-tuning involves adapting pre-trained models (e.g., embeddings models for retrieval and LLM for generation) to the specific domain.
– We fine-tune the generation model on question-answer pairs to improve response quality.
– We use domain-specific corpora to fine-tune the retrieval model for better semantic understanding.

To handle ambiguous or out-of-scope questions, we use a fallback response (e.g., “I don’t have enough information to answer that”) for out-of-scope queries. We can also implement a confidence scoring mechanism to identify low-confidence responses.

We use a scalable and low-latency deployment architecture that typically includes a cloud-based infrastructure for LLM (or a H100 GPU based server to operate locally a LLM) and a distributed retrieval system (e.g., Elasticsearch) for fast document lookup.

To evaluate and improve the system we can collect user feedback to identify areas for improvement. We can also use a combination of automated metrics (e.g., BLEU, ROUGE, retrieval accuracy) and human evaluation for qualitative assessment.