Sudhir Hasbe is President and Chief Product Officer at Neo4j, the graph database company powering 84 of the Fortune 100 (Walmart, Uber, Airbus) at $200M+ ARR and a $2B+ valuation. Before Neo4j, he ran product for all of Google Cloud's data analytics services: BigQuery, Looker, Dataflow, and led the Looker acquisition.

His thesis: the hallucinations we blame on AI models are really a data architecture problem. LLMs weren't trained on your enterprise knowledge, so handing them a data lake with 10,000 disconnected tables and asking them to reason is the wrong design. The fix is knowledge graphs: feeding the model a structured map of relationships, entities, and context so it can reason over meaning, not just vector similarity.

Sudhir breaks down the five capabilities knowledge graphs unlock for enterprise AI: GraphRAG (moving accuracy from 60% to 97%), semantic mapping across siloed systems, context graphs, agent memory, and multi-hop reasoning. He explains three architecture patterns customers are actually shipping, why giving an LLM hundreds of tools makes it worse, and what Uber, EA Sports, Klarna, and Novo Nordisk are doing differently.

This is the case for treating knowledge as infrastructure.

We cover:

Why enterprise AI needs a different playbook than consumer AI
The five data asset types every agentic system needs: system of record, historical, memory, context, and reference
How GraphRAG combines vector search and graph traversal to move from 60% accuracy to 95%+
Three architecture patterns: semantic layer only, semantic map plus domain data, full consolidation (the Klarna/Kiki model)
What context graphs capture that Salesforce doesn't: the Slack and email negotiation behind every deal
Why giving an LLM hundreds of tools drops accuracy, and how Uber uses knowledge graphs as a business validation layer
What Neo4j's Aura Agent, MCP server, and A2A support mean for developers starting today

Chapters:

(0:00) Why building a self-driving car is hard
(0:22) Intro
(2:03) Hallucinations as a data architecture problem
(4:31) From models-as-core to systems-of-knowledge
(6:13) Why data lakes fail AI agents
(9:15) The five data asset types enterprise agents need
(11:46) Where basic RAG breaks down: the Spotify metadata lesson
(16:00) GraphRAG: 3x accuracy, easier development, explainability
(18:47) Semantic mapping across the enterprise estate
(19:23) Three knowledge-graph architecture patterns
(22:42) Context graphs: capturing the "why" behind decisions
(25:33) Individual vs. organizational agent memory
(28:40) Multi-hop reasoning for fraud rings and AML
(31:52) Why there are no shortcuts in enterprise AI
(36:38) What happens when you give an LLM 100 tools
(39:19) The Uber example: knowledge graph as business validation
(44:42) First mile of a 26-mile marathon
(48:32) Aura Agent, MCP server, and the A2A protocol
(50:43) Where developers should start

Connect with Sudhir Hasbe:

LinkedIn: https://www.linkedin.com/in/shasbe/
Neo4j: https://neo4j.com/
Neo4j Aura: https://neo4j.com/product/auradb/

Connect with Chain of Thought host Conor Bronsdon:

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LinkedIn: https://www.linkedin.com/in/conorbronsdon/
YouTube: https://www.youtube.com/@ConorBronsdon

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at:
galileo.ai/mastering-multi-agent-systems

Hallucinations Are a Data Architecture Problem | Sudhir Hasbe, Neo4j

Chain of Thought | AI Agents, Infrastructure & Engineering

Hallucinations Are a Data Architecture Problem | Sudhir Hasbe, Neo4j