Sales Team Chatbot

AFP

afp-image

Discover our chatbot in action!

How does Kairntech Agentic RAG Chatbot work ?

1


Prototype quickly

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

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



  • Quote to come
    Nicolas Giraudon
    Group Marketing Director at AFP

Can Kairntech help to build your own Agentic RAG Chatbot solution?

We are here to help

A RAG chatbot solves the knowledge accessibility, accuracy, and efficiency challenges faced by businesses:
– Access to real-time, up-to-date information
– Efficient Knowledge Management & reduced employee search time
– Improved customer support & self-service
– Handling domain-specific & proprietary (and confidential!) data
– Reducing AI hallucinations & inaccurate responses
– Cost-effective scalability for enterprise knowledge
– Compliance & audit-friendly responses (sourced responses)

The chatbot can pull data from structured (databases, FAQs, manuals) and unstructured (PDFs, emails, news, medical records, audio, scientific articles, patents…) sources. Businesses should ensure data is clean and compliant with privacy regulations (e.g., GDPR).

The RAG chatbot should integrate via APIs with SharePoint, CMS/DAM repository, CRM (e.g., Salesforce), helpdesk (e.g., Zendesk), or internal databases. This ensures seamless access to real-time data and improves workflow automation.

Key metrics include:
– Time savings on specific tasks (faster responses, text génération…)
– Accuracy (correct responses – that could be improved via user feedback)
– Engagement (number of users and queries handled)
– Cost savings (reduced human agent workload)

There are different ways to maintain and improved chatbot over time:
– Regularly benchmark LLMs to select the most suitable one for the task
– Regular updates based on user feedback
– Monitoring performance logs are essential. A feedback loop with human agents can help refine responses and expand the knowledge base.