Graphwise joins DBTA for a roundtable on semantic layers and data products: the trust layer AI needs to stop guessing at what your data means.
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Event
Graphwise Talk #4: 5 Patterns of Enterprise AI Failure: How to Spot Yours?
In our fourth Graphwise Talk, data and AI practitioner Panos Alexopoulos and Jim Buonocore of #EPAM_Systems join Graphwise to unpack why enterprise AI projects stall: the failure patterns behind stuck pilots, why scaling is a data problem in disguise, and what it takes to build AI that compounds instead of stalling out.
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Event
KM + RAG: Building Trustworthy, Context-Aware AI
Hallucinated answers erode trust in AI fast. On September 29, Graphwise’s Kent Strocker joins KM + RAG: Building Trustworthy, Context-Aware AI to explore how verified, connected knowledge keeps retrieval accurate and traceable.
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Event
Graphwise Networking Evening in London
Join Graphwise team on June 10 for the casual evening over beverages, bites, and an exchange with people who are serious about where enterprise AI is heading, and what it actually takes to get there.
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Event
iiRDS as a Bridge Between DITA, Markdown and File Based Content for a Complete Information Provision
Break down content silos and connect multimodal documentation. Learn how iiRDS and GraphRAG enhance LLM-driven suppor, especially in safety-critical scenarios.
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Event
Talk: Using iiRDS to publish Intelligent Content as a Service
This session will show use cases on how iiRDS can be used as the backbone for delivery for creating intelligent content graphs (as the basis for digital twins) that offer content as a service by combining content and knowledge graphs. This should provide a basis for discussion and knowledge sharing.
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Event
Talk: Graph-Driven RAG AI Powered by DITA
This session will explain how DITA was used to automate the construction and maintenance of a scalable knowledge graph to drive generative AI applications along with a fully functional advanced neuro-symbolic chatbot that supports what other models lack: the ability to deterministic reasoning and inference.
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