How Modern AI Systems Really Find Answers: Build GraphRAG Applications
Overview
Move beyond vector search and build explainable AI systems that retrieve, reason and generate answers using knowledge graphs
This hands-on workshop takes you beyond basic RAG to build an explainable financial advisor using Neo4j, knowledge graphs, Cypher, public filings, news, and LLM agents. Led by bestselling author and Chief Scientist at GraphAware, Dr. Alessandro Negro, you’ll see how graph-backed AI systems handle multi-hop reasoning, entity relationships, time-based changes, and source-traceable answers that vanilla vector search often misses. You’ll work through a complete architecture for building auditable, production-minded GraphRAG applications for finance and other document-heavy domains.
What this workshop is about
Build a hands-on GraphRAG financial advisor using Neo4j and LLM agents
Move beyond basic vector RAG into graph-backed reasoning
Ingest corporate filings, news, and public financial data
Create a knowledge graph for entities, events, people, risks, and relationships
Use agentic retrieval, graph navigation, and text-to-Cypher
Answer complex financial questions with traceable, explainable reasoning
Who this is for
AI engineers building RAG and LLM applications
ML engineers moving into graph-backed AI systems
Data scientists working with complex document-heavy data
Software architects designing explainable AI applications
LLM app developers building production-grade assistants
Technical consultants working on financial, legal, compliance, or enterprise AI use cases
Tools and frameworks you will learn
Neo4j for knowledge graph storage and querying
Cypher for graph queries
Docling for document ingestion
LLM agents for iterative retrieval and reasoning
Vector search for semantic retrieval
Keyword and Lucene-style search for hybrid retrieval
Text-to-Cypher for natural language graph querying
LLM-based entity and relationship extraction
Entity resolution and graph enrichment techniques
LLM-as-judge and evaluation workflows
Why attend this NOW
Learn directly LIVE from bestselling author and data scientist
Get certified and receive complete recording and resources
Build a complete project, not isolated demos
Learn where plain RAG fails and how GraphRAG solves it
Work with realistic financial data, filings, news, and company information
See how to combine vector search, knowledge graphs, Cypher, and agents
Understand design decisions behind each architecture layer
Get explainability, traceability, and evaluation built into the workflow
Go beyond free tutorials that only cover basic RAG or toy examples
Learn a reusable architecture transferable to finance, legal, compliance, and healthcare
Prepare for the next wave of enterprise AI systems: graph-backed, agentic, and auditable
What you will get
Certificate of completion
Full HD recording
Presentation PPT
Ready-to-use GraphRAG architecture blueprint
Full working codebase shared upfront
Neo4j knowledge graph schema examples
Agentic retrieval workflow patterns
Text-to-Cypher prompt and guardrail patterns
Entity extraction and relationship extraction templates
Evaluation checklist for GraphRAG systems
Production-readiness checklist for financial AI advisors
Reusable design framework for graph-backed LLM applications
Pre-requisites
Comfortable with Python
Basic experience with LLMs
Familiarity with RAG concepts
Some understanding of embeddings and vector search
No prior Neo4j or Cypher experience required
No finance domain expertise required
About the speaker
Dr. Alessandro Negro is Chief Scientist at GraphAware, where he leads science and technology work around Hume, a mission-critical knowledge graph analytics platform. He is a bestselling author of celebrated graph-powered machine learning, knowledge graphs, and LLM books. His work focuses on knowledge graphs, LLMs, NLP, graph-aided search, recommendation systems, and explainable AI, with deep experience applying graph technologies to real-world enterprise and investigative use cases.