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 your enterprise data

Stop searching manually through documents or struggling with generic tools. With Kairntech, you query your enterprise data as you would a person.

  • 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

Take control of performance and continuously optimize how answers are generated.

  • Compare search results with generated answers
  • Evaluate performance across different configurations
  • Adjust every step of the process

Integrate seamlessly into your enterprise environment

Deploy the solution securely, connect it to your existing systems, and scale with confidence.

  • Industrialize at scale and embed using a rich REST API
  • Connect to your 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.

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.

Kairntech’s Document Question Answering solution enables organizations to ask questions directly to their internal and confidential documents. Unlike a conversational chatbot, it is not designed to engage in dialogue, but to deliver precise answers grounded in the documents themselves. This approach makes it much easier to leverage metadata and provides reliable, contextualized results through a combination of intelligent search and answer generation (RAG: Retrieval-Augmented Generation).

Yes. It can be used to query internal documents (PDF, PPT, Word files, databases, SharePoint, etc.) without exposing sensitive data to uncontrolled external services. Documents are indexed and vectorized internally to ensure that answers are generated exclusively from sources fully controlled by the organization.

Kairntech is designed with GDPR compliance in mind and supports on-premise or private cloud deployments. Fine-grained access control ensures that only authorized users can access documents or generated answers, maintaining strict data confidentiality.

Yes. The solution provides a full REST API, SSO support, and connectivity with enterprise content systems such as SharePoint, making it easy to integrate into existing applications or internal portals.

Absolutely. Kairntech enables the industrialization of document-based Q&A projects across multiple departments or use cases, with secure deployment options that support scalability, monitoring, and performance requirements typical of enterprise environments.

When the Document Question Answering solution does not find relevant information in the analyzed documents, it clearly indicates that no reliable answer can be provided. This helps avoid approximate answers and hallucinations, ensuring that results are strictly based on content actually present in your documents and their metadata.

Unlike generic integrations with public LLMs, Kairntech’s solution allows organizations to retain full control over their documents and models while ensuring data confidentiality, sovereignty, and regulatory compliance. This significantly reduces the risk of sensitive information being exposed to uncontrolled third-party services.