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/

In this episode, Amy Hodler catches up with Sheamus McGovern, the founder of the Open Data Science & AI Conference (ODSC), to celebrate the event's 10th anniversary. They track the evolution of the industry from the "data-centric" roots of 2015 to the current frontier of Agentic AI and GraphRAG.

- The AI Evolution: Why ODSC added AI to its name and how the community has shifted from math-first to vibe coding.

- Graphs & AI: The vital role of graph technology in providing memory and context for modern AI systems.

- The Hallway Track: Why in-person community and hands-on learning are the best therapy for tech professionals.

Find an upcoming ODSC event: https://odsc.ai/

Bryce Merkl Sasaki of G.V() sits down with David Hughes, Solutions Architect for Graph and AI at Enterprise Knowledge, during the Open Data Science Conference (ODSC) in San Francisco.

The conversation dives into the cutting edge of connected data, moving beyond the initial hype of Retrieval-Augmented Generation (RAG) to explore how graphs are essential for the next generation of AI.

• Multimodal GraphRAG: David explains the shift from simple associative search to a more robust approach that captures signals from images and audio by integrating knowledge management into the graph.
• Agentic Solutions & Observability: A look into the need for engineering rigor in AI agents, focusing on computation graphs, memory inspection, and moving toward deterministic returns using tools like BAML and DSPy.
• The Future of Graph Tech: David discusses the "uncomfortable but exciting" migration from native graph databases to graph-based modeling and querying within vector databases (like LanceDB).

Amy Hodler of GraphGeeks sits down with Weimo Liu, CEO of PuppyGraph, to discuss how they are changing the graph landscape. Unlike traditional databases, PuppyGraph is a graph engine that queries data directly where it lives—no data movement required. Key Highlights:

• Zero ETL: Query data lakes and warehouses (SQL, Delta Lake, etc.) as a graph without moving a single byte.
• Scalability: Designed for petabyte-scale analysis in industries like Cybersecurity, Anti-Fraud, and Healthcare.
• Simplify Graph-RAG by turning existing tables into a "knowledge brain" for chatbots.
• Fast Deployment: What used to take six months of data pipelining now takes just weeks via simple schema mapping.

https://www.puppygraph.com/

For years, Enterprise Architects viewed graph databases with a mix of curiosity and dread. Between specialist silos, complex ETL pipelines, and infrastructure friction, many chose to stay in the safety of relational tables.

Amy Hodler (Founder of GraphGeeks) chats with Max Latey (CEO of Pinboard Consulting) to discuss the shift in how organizations are adopting graph technology.

Key Takeaways:

• Graph on Relational: How tools are allowing EAs to sprinkle graph capabilities over existing stacks without heavy ETL.
• GraphRAG & LLMs: Why the push for GenAI is making graph technology a must-have for context-rich AI.
• Data Modeling: Why understanding self-edges is the key to identifying a true network in your data.
• Lowering the Skill Ceiling: How SQL 2023 and GQL are making graph accessible to standard data teams.

Pinboard Consulting specializes in high-impact graph solutions, entity resolution, and architectural strategy. https://www.pinboardconsulting.com/

Why is modern AI so "unruly"? According to information architect Jessica Talisman, it’s because we’ve over-indexed on data storage and ignored the art of description.

In this Graph Chat, Bryce Merkel Sasaki sits down with Jessica (Founder of The Ontology Pipeline) to discuss the bridge between Labeled Property Graphs (LPG) and RDF, the rise of neuro-symbolic AI, and why every tech company needs the perspective of a "Chief Librarian."

• Substack: Intentional Arrangement
• Framework: The Ontology Pipeline

Join David Hughes (GraphGeeks community) for a Graph Chat with Chang She, CEO and Co-founder of LanceDB, filmed at the ODSC conference.

Chang shares groundbreaking insights on how agentic retrieval systems are challenging traditional RAG approaches, requiring much higher throughput and iterative search. The conversation highlights the new Lance Format as the multimodal Lakehouse standard optimized for AI data operations.

Most excitingly for the graph community, Chang provides a first introduction on the new open-source project, Lance Graph, which enables storing graph schemas and executing Cypher-like queries directly on Lance tables, integrating vector, tabular, and graph data into a unified format.

Learn why data differentiation is the key to winning in the age of AI agents.

https://lancedb.com/

Join Amy Hodler of GraphGeeks and Henry Gabb, Chair of the newly renamed Graph Data Council (GDC) (formerly the Linked Data Benchmark Council - LDBC), for a deep dive into the world of vendor-neutral graph benchmarks, standards, and innovation.

Hear how the GDC is expanding its focus beyond traditional benchmarks like FinBench and Graphalytics to embrace microbenchmarks, synthetic data generation, and the exciting work being done on LEX schema to potentially unify property graphs and RDF. Learn why many members, including major vendors and researchers, value having a "seat at the table" to shape the future direction of the graph community.

Explore the intersection of data performance, complexity, and the drive for standard graph query languages and schemas essential for emerging AI applications like text-to-graph query.

https://ldbcouncil.org/

Amy Hodler and Dave Bechberger dive into the crucial role of memory in advanced AI systems, especially at the intersection of graphs, knowledge graphs, and generative AI.

Dave Bechberger, currently focusing on MCP servers, agentic memory, and semantic data layers, explains that memory is fundamental because standard LLM calls are atomic and lack recollection of prior interactions. An agent without memory lacks continuity for complex user interactions.

The discussion breaks down three key types of memory and how graphs apply:

• Episodic Memory: Transactional details are directly integrated into the context.
• Short-Term Memory: Session-based interactions that require compaction or summarization.
• Long-Term Memory: For extracting and storing patterns, trends, and preferences across multiple interactions.