The knowledge layer for enterprise: Processes
The knowledge layer for enterprise AI: Processes This series is the practical build companion to Jesús Barrasa’s knowledge layer manifesto.
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The knowledge layer for enterprise AI: Processes This series is the practical build companion to Jesús Barrasa’s knowledge layer manifesto.
A quick walkthrough from approval to your first running database. You’ve been accepted into the Neo4j Startup Program, congrats.
Turn accumulated learnings into procedures and user profiles, then feed them back into the agentPart 3 of the self-learning multi-agent series. Part 1 covered the tools and hooks.
As an AI application grows, its embeddings can quickly become one of its largest demands on memory. More documents, finer-grained chunks and richer embedding models all increase the amount of vector data your database needs to search.
Somebody asks whether GuardDuty covers every account. The question is about holes in the coverage, not about whether the service is switched on. From a flat resource inventory that is a genuinely annoying question.
Optimizing graph import partitioning based on workers and k-1 coloring algorithm.This article is a generalization of the approach designed by Eric MONK in his article “Mix and batch: a technique for fast, parallel relationship loading in...
The demo looked great. Stakeholders were impressed. Then someone asked the chatbot a real question, and it gave a confident, wrong answer.
Testing Jev (TypeSafe System One) with Navigation DecisonsI built neo4jev as an experiment to navigate a graph, here I explain a bit the why and how.Streamlit app in neo4jev that allows database config, source selection and different...
Check out what's new this week in Neo4j, including NODES 2026, Agent Memory, GraphRAG and Knowledge Layer