Graphs are the data model for connected data. Graph technology enables us to capture and compute over interdependent relationships. Join us to hear from experts and practitioners as we chat about the latest innovations and research.Visit GraphGeeks.org to learn more about our community.

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Setting the tone for GraphCon Seattle 2026, Brad Bebee (Director at AWS, Amazon Neptune) shares his perspective on the shifting landscape of graph technology. He explores why the rise of Generative AI and autonomous agents has transformed graphs from a &#34;vitamin&#34; (a nice-to-have clean data model) into a &#34;painkiller&#34; (an essential tool for AI accuracy, memory, and explainability).Key Topics:<ul><li>Why companies pay for graphs (and how Wiz leveraged graphs for explainability)</li><li>The shift from &#34;Vitamins&#34; to &#34;Painkillers&#34; in enterprise adoption</li><li>Why AI agents are driving a resurgence in ontologies and markdown graphs</li><li>How generative tools streamline knowledge graph creation</li><li>Q&amp;A: Source of truth vs. sidecar architectures</li></ul>

From Vitamins to Painkillers: The Business of Graphs & AI | Brad Bebee | GraphCon 2026

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At ODSC, Amy Hodler (GraphGeeks) chats with Stamati Liapis, cofounder of Enlay, about using graph AI and network modeling to transform risk management in the construction industry. Stamati explains how he applied his PhD in computational neuroscience (studying brain networks) to solving complex risk networks in construction.Key Takeaways: <ul><li>Around 96% of construction data sits unused. Graph AI and LLMs help structure this messy data for accurate future predictions.</li><li>We can combine graph modeling and LLMs to help contractors analyze financial, commercial, and contractual risk across a project’s entire lifecycle.</li><li>Academic Founder Tip: Shift from perfectionism and ship an 80%-ready product to get real-world user feedback instead of optimizing in a silo.</li></ul> More Info about Enlayehttps://www.enlaye.com/

Graph AI Meets Construction Risk: Stamati Liapis (Enlaye) at ODSC

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In this Graph Chat, Amy Hodler catches up with James Massaquoi at the Glasswing Ventures offices for a candid conversation on where knowledge graphs, AI agents, and enterprise data architectures are heading.Key Topics Covered:<ul><li>Context for Autonomous AI: Why reliable enterprise AI workflows depend on understanding historical context and connected data.</li><li>Cutting Through Buzzwords: Why &#34;context graphs&#34; shouldn&#39;t be overhyped and where graph technology actually delivers value in the modern stack.</li><li>Knowledge Graphs - Memory Stores: How the line between graph databases, auxiliary memory models, and agent architectures is blurring.</li><li>Building for Enterprise Scale: What early-stage investors look for in AI and knowledge graph startups today.</li></ul> 🔗 More on Glasswing Ventures: https://glasswing.vc/

Why AI Agents Need Knowledge Graphs: A Chat with Glasswing Ventures’ James Massaquoi

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In this interview live from the Open Data Science Conference (ODSC) in Boston, Amy Hodler (GraphGeeks) sits down with Dr. Janet Six, Senior Product Manager at Tom Sawyer Software, to discuss the evolving role of data visualization and graph technology in the age of AI. Dr. Six highlights why data visualization shouldn&#39;t just be an afterthought—like sticking a basic pie chart into a slideshow—and advocates for a systems engineering approach to craft interactive, meaningful visual tools. They explore how visualization is essential for monitoring and managing complex AI agent workflows, as well as providing the deep context that simple LLM text responses often miss.

Data Vis & Graphs in the Age of AI with Dr. Janet Six

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Are you feeling anxious about the future of AI and your job? In this GraphGeeks chat, Amy Hodler catches up with Kathryn (Kat) Erickson, co-author of Tech Confidential, live from the Open Data Science Conference (ODSC).Kat shares how the tech industry shifted from excitement to fear, why bypassing failure with AI actually kills our creative fulfillment, and practical, &#34;Zen&#34; strategies to combat career anxiety through action and writing.Key Takeaways<ul><li>Mental Toughness in Tech: Tech Confidential tools for maintaining your confidence and identity at any stage of the tech journey. https://www.techconfidential.ai/ </li><li>The Value of Failure: AI lets us skip the trial-and-error process, but we risk missing the unexpected breakthroughs and fulfillment that come from solving hard problems.</li><li>Action Combats Fear: You can&#39;t control market shifts, but you can control your response. </li><li>Reclaim the Explorer Mindset: AI has leveled the playing field. Instead of fearing the next 10 years, tech professionals have the power to actively build a better future.</li></ul>

Defeating AI Anxiety & Reclaiming Your Tech Career with Kat Erickson

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Amy Hodler takes a moment with Paco Nathan (Senzing) at the Open Data Science Conference (ODSC) to chat about the shift happening in Graphs and AI.From packed tutorial rooms to hallway conversations, graphs are no longer just for academics—they are driving real-world business cases. Paco breaks down his ODSC workshop on entity resolution, and combining graph algorithms with vector stores. Key Takeaways:<ul><li>The Business Pivot: Why technical teams are shifting focus from just building knowledge graphs to solving immediate business problems.</li><li>Graph + AI Integration: How entity resolution generates the clean data needed for advanced graph tech and downstream AI apps.</li><li>Why ODSC is the Sweet Spot: A look at the thriving community, networking, and the future of growing graph focus.</li></ul>

Chat: The Business Pivot in Graph Tech with Paco Nathan

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In this Graph Chat, Amy Hodler catches up with graph expert and consultant Clair Sullivan at the Open Data Science Conference to talk about Entity Resolution, GraphRAG, and catching fraud. Learn how concepts that used to be highly niche—like ontologies, semantics, and context graphs—are now entering mainstream tech workflows. And hear why we need to stop &#34;vibe coding&#34; everything and use knowledge graphs to deliver high-fidelity results.More Info on Clair and what she does: https://github.com/cj2001

Graph Chat: Graphs & GenAI with Clair Sullivan

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Listen to a quick hallway at the Open Data Science Conference (ODSC) with Amy Hodler and Daniell Stevens, Product Analytics at Adobe.In this impromptu conversation, Dan shares his takeaways from the conference, including a trick for using SQL to bridge the definition gap between data science and engineering teams. We also dive into his personal history with graph path languages, how graph technology is colliding with LLMs to solve messy data problems like entity resolution, and why getting out of your day-to-day silo at events like ODSC is so critical for data practitioners.

Graph Chat: Graphs, LLMs, and Data Science with Dan Stevens (Adobe)

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How do we move past the ultimate graph answer: &#34;It depends&#34;?Live from the Open Data Science Conference (ODSC), Amy Hodler sat down with Golven Leroy, graph researcher and data science lead at Graphable, to discuss groundbreaking core mathematics that could change how we calculate the compute cost, latency, and bounds of graphs and trees—independently of technology.We also dive into his top takeaways from ODSC, including tracking LLM experiments with MLflow, revolutionary chip/circuit design, and open-source frameworks like Agor for visualizing enterprise AI agents.Link to the referenced paper: https://msp.org/involve/2026/19-2/p05.xhtml

Graph Chat with Golven Leroy on Predicting Graph Costs & Moving Past "It Depends"

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Is the future of AI fluid and semantic, or rigid and structured? In this episode of Graph Geeks in Discussion, host Amy Hodler sits down with Roie Schwaber-Cohen, Head of DevRel at Pinecone. Together, they explore a fascinating philosophical and operational debate: the intersection of vector geometry (semantic distance) and graph topology (explicit connectivity). Tune in to discover why solving the next generation of AI challenges, from hallucinations to agentic long-term memory, requires fusing both worlds, and why we all might just need to &#34;embrace the fuzziness.&#34;

Geometry vs. Topology in AI: Roie Schwaber-Cohen (Pinecone) & Amy Hodler

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Is data copying dead? In this episode of the GraphGeeks Podcast, host Amy Hodler sits down with Marko Budiselić (&#34;Buda&#34;), Co-founder &amp; CTO at Memgraph, to discuss their major new releases: Memgraph Zero and MemGQL.Discover how Memgraph is tackling the massive pain point of ETL pipelines by creating a federated GQL layer that queries data directly at the source—across Postgres, MySQL, Neo4j, ClickHouse, and more. Buda also shares how a centralized semantic layer is becoming essential for the future of AI agents.Key topics covered:<ul><li>The core architecture and vision behind Memgraph Zero.</li><li>How MemGQL acts as a federated graph query engine.</li><li>Moving beyond context graphs into environmental graphs for AI agents.</li><li>The shift toward agentic data modeling and mapping.</li></ul> 👉 Read more about the release: https://memgraph.com/docs/memgraph-zero 👉 Vote on the next data connectors in the Memgraph Community Roadmap Poll: forms.gle/2MLfWp24uwbJpsey8

Memgraph Zero & Federated GQL with Marko Budiselić

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In this chat, Amy Hodler catches up with William Lyon to discuss his recent return to the Neo4j Product Team and his deep dive into the world of AI agent frameworks and memory.Wil discusses his hands-on workshop exploring a fascinating frontier: Graphs as the Memory for AI Agents. Moving beyond simple retrieval, Will explains how we can use graph technology to mirror human cognitive functions, including:<ul><li>Episodic Memory: Learning from user interactions.</li><li>Semantic Memory: Building a canonical model of the world.</li><li>Procedural Memory: Unlocking advanced, graph-based reasoning for agents.</li></ul> Whether you&#39;re interested in the latest in GraphRAG, the evolution of Knowledge Graphs in the age of LLMs, or just want to hear about Will’s journey through the startup ecosystem, this conversation is packed with insights that have only become more vital since they were recorded.

Graph Chat with William Lyon on AI Agents, Graph Memory, and the Return to Neo4j

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Bryce Merkl Sasaki (Head of Marketing at gdotv) sits down with the &#34;Gandalf of Graph Technology&#34; himself, Paco Nathan (Senior DevRel at Senzing), at the Open Data Science Conference. Paco is a pioneer in neural networks and NLP since the 1980s, and a leading voice in the MLOps and Graph communities. In this chat, Paco cuts through the current hype bubble to discuss how graph technologies and entity resolution are solving high-stakes, real-world problems. From interdicting $3 trillion in dark money and human trafficking to fixing systemic data disconnects in government agencies through the NIEM semantic standard, this conversation explores the profound human impact of well-engineered data.Key Topics:<ul><li>Entity Resolution (ER): How connecting data points protects real people in the legal and financial systems.</li><li>Semantic Standards: Using NIEM and SKOS for better context engineering</li><li>Why the biggest bottleneck in AI isn&#39;t the math—it&#39;s designing UX &amp; visualizations that humans can actually use.</li><li>The AI Bubble: Paco’s perspective on the current industry jitters vs. the tangible tech that is ready for production.</li></ul>

Graph Chat with Paco Nathan on AI as a Practice, Not a Product

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What happens when two community-builders sit down at the Open Data Science Conference (ODSC)? They talk about the future of AI infrastructure and the importance of supporting the next generation of founders!In this episode, Amy Hodler of GraphGeeks is joined by Michelle Yi, co-founder of Generationship, to dive into:<ul><li> The &#34;Eval&#34; Crisis: Why 95% of GenAI POCs fail and how rigorous evaluation is the cure.</li><li>Causality &amp; Graphs: Moving beyond &#34;ice cream and shark attacks&#34; to understand the why behind AI predictions.</li><li>Spatial Reasoning: How graphs are becoming essential for robotics and multimodal AI.</li><li>Generationship: Michelle’s mission to fund and support early-stage female+ founders building the future of AI infrastructure.</li></ul> Connect with Michelle &amp; Generationship:🔗 https://www.generationship.ai/

Graph Chat with Michelle Yi on AI Evals, Causal Graphs and Community

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In this Graph Chat from ODSC, Sony Green (COO of Kineviz) joins Bryce Merkl Sasaki to discuss how graph technology is moving from a niche tool to a mainstream enterprise powerhouse. Highlights:<ul><li>The Spanner Graph Impact: Why Google’s entry into the graph space is a watershed moment for big data and high consistency.</li><li>Human-Centric AI: A look at Knowledge Mapping—helping law enforcement and investigators find absolute truths in unstructured data without relying on AI-generated conclusions.</li><li>No-Code Graphing: Introduction of the Graph Composer, a tool designed to map disparate data sources into a graph model with zero coding.</li></ul> Learn more about Kineviz: https://www.kineviz.com/

Graph Chat with Sony Green on the Evolution of Graph Intelligence

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In this episode of Graph Geeks, recorded live at the Open Data Science Conference (ODSC) in San Francisco, Bryce Merkl-Sasaki sits down with graph pioneer Denise Gosnell, PhD. Drawing from her experience at DataStax and AWS Neptune, Denise shares why graph technology is the secret engine behind modern AI. Key Highlights<ul><li>Denise argues that the current AI explosion isn&#39;t a separate trend but is actually building off previous innovations like graphs that now provide the essential context that AI systems require to function effectively.</li><li>Successful graph-centric companies often see massive valuations because graph technology provides the shortest conceptual path from a business idea to technical implementation, allowing teams to ship faster and align more clearly.</li><li>Denise discusses her new book, Tech Confidential: The Insider’s Playbook for Daring Entrepreneurs. It offers a roadmap for the tech life cycle, covering everything from managing professional egos and building collaborative teams to navigating successful company exits.</li></ul> https://www.techconfidential.ai/

Graph Chat with Denise Gosnell on ROI, AI, and the Power of Connections

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Live from ODSC West (Open Data Science Conference), Amy Hodler of GraphGeeks chats with Wes Madrigal, Co-Founder and CEO of Kurve, to discuss the intersection of graph technology, metadata, and the future of AutoML.Key Points:<ul><li>Solving the 80% Problem: Despite AI advancements, most effort still goes into data discovery. How to automate extracting metadata like foreign keys from data lakes to build a relationship graph.</li><li>Graph-Based Computation: Wes describes a computational graph where tables are nodes and foreign keys are edges. This turns data preparation into a graph traversal problem, making it faster to roll up data to the right granularity.</li><li>The Return of Facts: As GenAI matures, experts are realizing that text-to-SQL and agents fall short without a robust ground truth. Ontologies and relational metadata are seeing a resurgence as the essential facts AI needs to function. </li></ul> More data and demo at https://kurve.ai/

Graph Chat: Automating Data Discovery with Wes Madrigal

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In this in-person chat at ODSC, Amy Hodler (GraphGeeks) and Bryce Merkl-Sasaki (gdotv) discuss how the graph space has evolved from simple nodes to the &#34;new horizon&#34; of multimodal GraphRAG and AI procedural memory.In this video:<ul><li>Moving beyond text to model audio and video in graphs.</li><li>How graphs extend AI context windows and create long-term procedural memory for agents.</li><li>Better Tooling: Bryce shares how gdotv simplifies the graph stack for developers with better debugging, visualization, and schema scanning.</li><li>Why we still need old-fashioned analytics to understand our data.</li></ul> Learn more about gdotv: https://gdotv.com/

Graph Chat: Can graphs give AI better memory? 🧠

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In this Graph Chat, GraphGeeks founder Amy Hodler sits down with Meg Paulosky, Director of Data and Analytics at Stitch Fix, live from the Open Data Science Conference (ODSC).For over a decade, Stitch Fix has pioneered the blend of human styling and data science. Now, Meg explains how her team is evolving that mission by integrating Knowledge Graphs to better understand their data landscape and inspire the &#34;style journey&#34; for both new and existing clients.<h3>Key Discussion Points:</h3><ul><li>Proactive Data Leadership: How Stitch Fix uses graphs to move beyond reactive reporting to being aware of data trends before they surface in a dashboard.</li><li>The Evolution of AI at Stitch Fix: Exploring the intersection of human intuition, generative AI, and graph structures to fulfill client promises.</li><li>ODSC Highlights: Meg’s &#34;aha moment&#34; regarding agent design and the future of AI infrastructure.</li></ul> Whether you are a data leader or a fashion-tech enthusiast, this conversation offers a unique look at how one of the most data-driven retailers in the world is staying ahead of the curve.

Graph Chat: Stitch Fix & Meg Paulosky on Redefining Style with Knowledge Graphs

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Is the AI moment actually a Graph moment? In this chat, Amy Hodler (Founder of GraphGeeks) sits down with Philip Rathle, CTO of Neo4j, to discuss the massive shift in how enterprises are building AI. In this interview, they dive into:<ul><li>Graph RAG vs. Vector Search: Why similarity search isn&#39;t enough for high-stakes enterprise problems and how Graph RAG provides the discernment LLMs lack.</li><li>Neuro-symbolic AI: A look at the locus of reasoning and how combining non-deterministic models with deterministic graph data creates a gray box of explainability.</li><li>The Memory Problem: Why agents need meta-knowledge and a long-term memory that doesn&#39;t involve shoving an entire company’s database into a model&#39;s context window.</li><li>Neo4j’s Infinigraph: A peek at vertical sharding and how Neo4j is tackling 100-terabyte graphs.</li></ul> More on Neo4j at <a href="https://www.youtube.com/redirect?event=video_description&q=https%3A%2F%2Fneo4j.com%2F&redir_token=QUFFLUhqbmZaNjhpZWpCdTV3aTdONmJpVXBOOERHejZtQXxBQ3Jtc0tsRmZ5ZGI3TjB2Vll1SjliR0YzMkVuOTNCb0xIQ2dyODctTnl2NUg0VDVRc0ZKS3pTekVBZWY0bmQ4dWVsYkJIV3kyX01rTlpuVVI0NXl6STg1cERzSndDdjZmcVpTdXpkMUlHYmlLRl9EODZ3MElBOA&v=JgNBy5k_fTU" rel="nofollow">https://neo4j.com/</a><a href="https://www.youtube.com/@GraphGeeksOrg" rel="nofollow"><img src="https://yt3.ggpht.com/gx0TnYB0tB6x4lSbJ2L9Qa7BTYMEcsCfOOJ8QpmfOoPVAf6zEzRsEYAr82-IO7U97uknZU-q3A=s88-c-k-c0x00ffffff-no-rj" width="36"></a>

Graph Chat: Neo4j’s Philip Rathle on Neuro-symbolic AI and Infinigraph

Graph Tech, Graph Analytics, and Knowledge Graphs

Graphs are the data model for connected data. Graph technology enables us to capture and compute over interdependent relationships. Join us to hear from experts and practitioners as we chat about the latest innovations and research. 

Visit GraphGeeks.org to learn more about our community.