Agentic RAG Chatbots

Turn siloed documents into smart chatbots — customized, scalable, and designed to boost productivity.

Can Kairntech’s AI create Agentic RAG Chatbots from your documents?

Turn your enterprise data into trusted conversations

Give your teams and clients instant access to reliable answers, grounded in your own data.

  • Enable contextual, multi-turn conversations
  • Gain trustworthiness with accurate, well-sourced answers
  • Leverage all your relevant data sources

Configure Agentic RAG Chatbots easily

Ensure every question is interpreted correctly and matched with the most relevant content from your enterprise data.

  • Make your chatbot understand your business language
  • Improve answer accuracy through smarter retrieval
  • Automatically enrich questions for better answers

Keep your chatbot reliable over time

Monitor performance, fix issues quickly, and continuously improve answer quality.

  • Track how users interact with the chatbot
  • Identify and fix poor answers
  • Update and improve your system continuously

Want to learn more?

How do Kairntech Agentic RAG Chatbots 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 document metadata, search methods, embedding models, retrievers, LLM prompts, agent & tools 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.

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.

A Kairntech Agentic RAG Chatbot is an enterprise AI solution that combines information retrieval with intelligent agents and tools to respond to complex natural language queries using internal data sources (documents, databases, APIs). This architecture goes beyond simple dialogue: it plans, retrieves relevant information, interprets it, and generates reliable answers based on actual sources.

A classic AI chatbot mainly responds to questions using its internal knowledge from the language model, with limited control over the sources it relies on and potentially outdated or incomplete knowledge.

A Kairntech Agentic RAG Chatbot goes further: it leverages your documents, data, and business rules to find relevant information, verify it, and provide a reliable answer. It doesn’t just “chat”, it follows a structured reasoning process to deliver useful, actionable responses in a professional context.

This solution helps organizations to:

  • Increase team productivity by automating search and response tasks.
  • Quickly access relevant information, even across large volumes of data,
  • Reduce errors or inaccurate answers thanks to an architecture based on real sources,
  • Handle complex business use cases (specialized customer support, internal documentation, automated workflows)

Both unstructured data (PDFs, emails, manuals) and structured data (databases, CRM) can be integrated via APIs or connectors. The Agentic RAG Chatbot indexes, segments, and leverages these sources to provide contextualized answers, while also enabling the use of business metadata to refine the relevance of results.

Yes. Kairntech is a European solution designed to meet enterprise requirements for security, confidentiality, and regulatory compliance (GDPR). Deployments can be on-premise or on a private European cloud, with granular access rights, ensuring that only authorized users can query sensitive data and documents.

A standard RAG system retrieves relevant information before generating an answer. An Agentic RAG adds intelligent agents capable of breaking down a query into multiple steps, orchestrating tools, refining searches, and performing intermediate tasks, which improves answer quality in complex enterprise contexts.

The Agentic RAG Chatbot integrates via connectors and APIs with internal systems (CRM, databases, document repositories) to leverage real-time information and harmonize business workflows.

Success is typically measured by:

  • Speed of information access (time saved),
  • Accuracy of answers,
  • User satisfaction,
  • Effort saved compared to manual searches.