Resource library
Document Object Model (DOM) Graph RAG
DOM Graph RAG addresses the limitations of vector-based RAG models. Read how it can improve the accuracy of AI-generated content.
Conversational AI for the Workplace
Introducing knowledge-hub.eco, a platform that’s based in Graph RAG to give you advanced Conversational AI for the Workplace.
Retrieval Augmented Generation (RAG) for the Workplace
This on-demand webinar explores an innovative solution: Semantic RAG, which harnesses the power of knowledge graphs, large language models (LLMs), and graph databases to enhance semantic retrieval and generation capabilities.
Generative AI Starter Kit
Our Generative AI Starter Kit lets you build a prototypical Gen AI system using your own data with the help of professionals.
PoolParty for SharePoint Bundle Release 2024
Watch this bundle release video where Alexi Lopex-Lorca, Associate Director of Professional Services & Product Manager of PoolParty for SharePoint Integration, and Helmut Nagy, Chief Product Officer, show you how to get the most our of your SharePoint experience with one package.
PoolParty for SharePoint and WAND Taxonomies
Watch this recorded webinar on how WAND-curated taxonomies can jumpstart the PoolParty for SharePoint journey of digital workers.
PoolParty Release 2024
PoolParty 2024 is here and we’ve put all the information you need in one place. Catch up on our content and celebrate this release with us!
The Seven Cases for Knowledge Graph Integration in a RAG Architecture
This paper discusses the limitations of vector-based RAG and offers up seven beneficial cases for knowledge graphs in RAG architecture.
PoolParty Product Bundles
This slide deck gives information about the PoolParty Product bundles, the pain points that the bundles help remedy, and how to get started.
Optimizing LLMs with RAG
Watch this discussion on how to optimize the effectiveness of LLMs with the help of Retrieval Augmented Generation (RAG).
Generative AI Powered by Knowledge Graphs
In this webinar Andreas Blumauer dive into details of Semantic RAG, such as: Overcoming the limits of LLMs, Combining LLMs with other tools to create “AI you trust.”, Building Generative AI on a strong Semantic base, Developing a Domain Knowledge Model, and Explaining Semantic Search and Vector Search in Generative AI.