Markdown source

AI on Your Lakehouse: Context Comes in Shapes, Not Queries

Conference Context

Session Description

Your agent can reach your data but still can't use it reliably: vector search and Text2SQL each hand it a slice, but not the view to know what's truly relevant and how to connect the right info. Without that, answers come back confident but wrong, and agent decisions cannot be trusted. The problem isn't caused by a bad model or bad query, but rather a lack of context, and thinking in terms of shapes is what cracks it. In this hands-on session, you'll learn how to build three reusable graph shapes from your lakehouse data using Neo4j, so your agent can navigate and view the right context to answer and act accurately: - Table of Contents (Trees) — navigate what's there - Themes (Communities) — surface patterns nobody named - Connections (Paths & Cycles) — trace how entities, documents, and records relate Portable to BigQuery, Databricks, Snowflake, or anywhere. You'll leave with real, practical techniques and the code to run with your own data and agents.

Media Evidence

Why your agents need decision traces, not just documents — Zach Blumenfeld, Neo4j (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

Evidence Graph

This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

Media Signals

Agent Reading Notes

Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

Transcript Status

Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Cached at raw/sources/youtube-transcripts/B9h9ovW5H9U.txt (2,859 words).

People

Supporting Slides

Slide Evidence

Attendance Visibility

No high-confidence attendance icon signal is shown for this talk. The sampled video evidence was either low confidence, source-proxy-only, or did not expose a clear audience view.

Synthesis

Synthesized Breakdown

So, my name is Zach. Uh I work for Neo4j. We're a graph intelligence company. You can think of us like a knowledge layer graph database at the core.

Speaker And Company Context

Topics Covered

Derived Links And Source Material

Novel Concepts / Clever Methods

Evidence Boundary

This synthesis uses the official schedule plus cached video transcripts. Official AI Engineer World's Fair San Francisco 2026 livestreams and cut videos are primary event video sources for transcript/slide evidence; external, historical, or speaker-matched videos remain supporting context unless manually verified as exact official event recordings.