Conversational AI for the Workplace

Using Graph RAG to implement a trustworthy and high performing Conversational AI application

Conversational AI has been transforming the way employees access information in the workplace, enabling them to engage with machines in a more “natural,” sophisticated way. The market for these platforms has soared, producing a lot of products that are decent but to be frank, not great – they often provide inadequate, confusing answers based on sources you can’t trace back. 

Conversational AI for the workplace should ensure that answers are not only precise, they also are comprehensive and able to provide employees with the most relevant information to make important decisions and complete tasks. Enter Graph RAG, a technological approach which combines knowledge graphs and large language models (LLMs) to strengthen the output of Conversational AI. Graph RAG uses natural language processing and semantic search to find documents based on the user’s intent of the query, as well as generates new content about this topic while in dialogue with the “bot.” 

The PoolParty Team has put together its own Conversational AI for the workplace called knowledge-hub.eco, a demo application that allows you to leverage the high conversational performance of an LLM with the reliability and trust of a knowledge graph.   

This booklet gives you a tour of knowledge-hub.eco’s 5 core features and also dives into:

• The main benefits of the Graph RAG approach
• Practical use cases for Conversational AI applications like knowledge-hub.eco
• The Gartner Hype Cycle for Generative AI 
• How to get started quickly