Document Question Answering

Build Q&A assistants for enterprise documents using RAG — fast, smart and secure.


Can Kairntech RAG assistants create business impact from your documents?

Ask questions to enterprise data

rag-experiment
  • Ask questions using natural language
  • Use metadata to filter and obtain accurate answers
  • Gain trustworthiness by linking and viewing sources

Experiment, customize and enhance quality

  • Compare search results with generated answers
  • Evaluate quality across various settings
  • Customize extensively any component of the value chain
rag-assistant-comparison

Integrate, industrialize and monitor

scalability
  • Industrialize at scale and embed using a rich REST API
  • SSO and connectivity to content systems (Sharepoint…)
  • Deploy on premise including LLMs

Want to learn more?

How do Kairntech RAG assistants work ?

1


Prototype quickly

Uploaded documents are indexed, segmented and vectorized automatically.


Start asking questions straight away!

2


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.

3


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.

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


  • “ 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

  • ” 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’s RAG assistant help to build your own question answering solution?

We are here to help

Advanced IR systems use techniques like:
– Full-text search to match keywords
– Semantic Search (e.g., vector embeddings, cosine similarity) to match meaning not just keywords
– Hybrid Search (combining keyword + vector search for better recall)
– Re-ranking models (like cross-encoders) to refine results.

In addition, we offer an advanced facet filtering feature (including AND, OR operators…) to select the most relevant chunks to be sent to the LLM.

Yes! RAG systems can index and retrieve data from:
– Internal databases like SharePoint
– PDFs, Word docs (processed via text extraction)
– APIs & real-time data sources
This allows employees to get AI-powered answers without exposing sensitive data to public models.

To handle out-of-scope questions, we use a fallback response (e.g., “I don’t have enough information to answer that”). 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.