Resources
KM + RAG: Building Trustworthy, Context-Aware AI
Hallucinated answers erode trust in AI fast. Watch the webinar KM + RAG: Building Trustworthy, Context-Aware AI to explore how verified, connected knowledge keeps retrieval accurate and traceable.
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What Is a Context Layer? The Missing Layer in Enterprise AI
A context layer is an infrastructure component that gives AI applications access to metadata, business definitions, lineage, entity relationships, and domain-specific knowledge alongside enterprise data.
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Applications and Use Cases of Natural Language Processing (NLP)
Natural language processing becomes most useful when language is connected to a specific task, such as interpreting a voice command, extracting clinical information, or analyzing customer sentiment.
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Natural Language Processing (NLP) Explained: Foundations and Architecture
Processing human language is difficult for machines because meaning depends on context, grammar, intent, domain knowledge, and ambiguity.
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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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The Context Layer: The Emerging Stack for Context-Aware AI
Watch the replay of our Data Science Connect roundtable on the emerging context layer, then a Q&A with Andreas Blumauer.
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What is an LLM Hallucination?
A Large Language Model (LLM) hallucination is when a LLM generates factually incorrect or senseless information that looks grammatically correct and feels confident.
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Two Paths to Self-Improving AI Agents and Why One Works
Disconnected data is silently breaking enterprise AI agents. Alan Morrison compares two fixes: GraphRAG vs. recursive self-improvement, and what each means for your team.
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Graphwise Talk#3: Under the Hood of Trustworthy AI: Memory, Meaning, Infrastructure
In our third Graphwise Talk, ontologist Kurt Cagle joins Graphwise to get under the hood of trustworthy AI — how ontologies encode meaning, how semantic memory makes AI auditable, and what it actually takes to build systems that can explain themselves.
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Making Product Knowledge AI-Ready: Lessons from KAESER
Product data locked in SAP and siloed systems stops AI initiatives in their tracks. KAESER and PANTOPIX reveal how they built a unified semantic backbone to turn complex product knowledge into a live, AI-ready application.
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AI-Assisted Taxonomy Creation: Tools, Workflows, and Where Do Humans Fit In?
AI is changing how taxonomies are built, but what does that actually look like in day-to-day practice? This webinar reveals where AI speeds up taxonomy building, where human expertise is non-negotiable, and how both collaborate effectively.
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The Missing Layer Between Scientific Data and Breakthrough Ideas
R&D teams spend too much time searching systems instead of making breakthroughs. Graphwise and Datavid show how a semantic backbone unifies fragmented research data without requiring a costly infrastructure overhaul.
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