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/

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/