Knowledge Graph

29 posts · 10 sources

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AI EngineerYouTube4d

Turning Agent Memory Into Skills That Work — Will Lyon, Neo4j

GleanBlog4w

From enterprise search to enterprise context: what AI agents actually need

DatabricksBlog3 Sept

Governance beyond security: knowledge, context & ontology on the lakehouse

GleanBlog20 Aug

Retrieval-augmented generation (RAG) for LLMs

GleanBlog10 Aug

Why a unified document model is essential for a comprehensive workplace ChatGPT

GleanBlog10 Aug

How knowledge graphs work and why they are the key to context for enterprise AI

GleanBlog10 Aug

What sets great retrieval augmented generation apart — and why vector search isn’t enough for AI

GleanBlog10 Aug

Is MCP + federated search killing the index?

GleanBlog10 Aug

Enterprise search is hard: why it’s so behind—and what it’ll take to catch up

GleanBlog10 Aug

So, your AI vendor is building enterprise connectors — here are 9 questions to differentiate real enterprise AI systems

GleanBlog28 Jul

Enterprise context starts with indexing, but it doesn’t end there

GrabBlog24 Jul

Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions

GitLabBlog25 Jun

Google Antigravity agents get full context with GitLab Orbit

GleanBlog24 Jun

Your AI coworker in Slack with context across every system

TemporalBlog10 Jun

Diving into the AI iceberg: What lies beneath your AI agents

AirbnbBlog19 May

Scaling Airbnb’s identity graph with a unified knowledge graph infrastructure

GleanBlog12 May

Context makes the Coworker: Glean preferred ~2.5x as often as off-the-shelf MCP tools, which consumed 30% more tokens in Claude Cowork

QdrantBlog22 Apr

How Data Graphs Built a True Hybrid Graph RAG Platform

GleanBlog19 Mar

Knowledge graph vs vector database: how to choose your AI foundation

GleanBlog11 Feb

Not all enterprise context is created equal: Glean results are preferred ~2× more than ChatGPT and 1.6× more than Claude