Is data copying dead? In this episode of the GraphGeeks Podcast, host Amy Hodler sits down with Marko Budiselić ("Buda"), Co-founder & 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:

• The core architecture and vision behind Memgraph Zero.
• How MemGQL acts as a federated graph query engine.
• Moving beyond context graphs into environmental graphs for AI agents.
• The shift toward agentic data modeling and mapping.

👉 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

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:

• Episodic Memory: Learning from user interactions.
• Semantic Memory: Building a canonical model of the world.
• Procedural Memory: Unlocking advanced, graph-based reasoning for agents.

Whether you'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.

Bryce Merkl Sasaki (Head of Marketing at gdotv) sits down with the "Gandalf of Graph Technology" 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:

• Entity Resolution (ER): How connecting data points protects real people in the legal and financial systems.
• Semantic Standards: Using NIEM and SKOS for better context engineering
• Why the biggest bottleneck in AI isn't the math—it's designing UX & visualizations that humans can actually use.
• The AI Bubble: Paco’s perspective on the current industry jitters vs. the tangible tech that is ready for production.

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:

• The "Eval" Crisis: Why 95% of GenAI POCs fail and how rigorous evaluation is the cure.
• Causality & Graphs: Moving beyond "ice cream and shark attacks" to understand the why behind AI predictions.
• Spatial Reasoning: How graphs are becoming essential for robotics and multimodal AI.
• Generationship: Michelle’s mission to fund and support early-stage female+ founders building the future of AI infrastructure.

Connect with Michelle & Generationship:

🔗 https://www.generationship.ai/

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:

• The Spanner Graph Impact: Why Google’s entry into the graph space is a watershed moment for big data and high consistency.
• 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.
• No-Code Graphing: Introduction of the Graph Composer, a tool designed to map disparate data sources into a graph model with zero coding.

Learn more about Kineviz: https://www.kineviz.com/

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

• Denise argues that the current AI explosion isn'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.
• 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.
• 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.

https://www.techconfidential.ai/

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:

• 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.
• 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.
• 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.

More data and demo at https://kurve.ai/

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 "new horizon" of multimodal GraphRAG and AI procedural memory.

In this video:

• Moving beyond text to model audio and video in graphs.
• How graphs extend AI context windows and create long-term procedural memory for agents.
• Better Tooling: Bryce shares how gdotv simplifies the graph stack for developers with better debugging, visualization, and schema scanning.
• Why we still need old-fashioned analytics to understand our data.

Learn more about gdotv: https://gdotv.com/

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 "style journey" for both new and existing clients.

• 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.
• The Evolution of AI at Stitch Fix: Exploring the intersection of human intuition, generative AI, and graph structures to fulfill client promises.
• ODSC Highlights: Meg’s "aha moment" regarding agent design and the future of AI infrastructure.

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.

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:

• Graph RAG vs. Vector Search: Why similarity search isn't enough for high-stakes enterprise problems and how Graph RAG provides the discernment LLMs lack.
• 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.
• The Memory Problem: Why agents need meta-knowledge and a long-term memory that doesn't involve shoving an entire company’s database into a model's context window.
• Neo4j’s Infinigraph: A peek at vertical sharding and how Neo4j is tackling 100-terabyte graphs.

More on Neo4j at https://neo4j.com/