Sudhir Hasbe, Neo4j's President and Chief Product Officer, on the strategic shift behind the GraphAware deal, what "open ...

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

Enterprise AI deployments face significant challenges at the database layer rather than the model layer. The data stack was not designed for AI agents, resulting in rising costs and inefficiencies. A ...

Abstract: The paper describes an approach that combines work from three fields with previously separate research commu-nities: social robotics, conversational AI, and graph databases. The aim is to ...

One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is "hallucination"—the generation of plausible-sounding but factually incorrect information. KAIST ...

Michael Down, Global Head of Financial Services at Neo4j, tells RBI Editor Douglas Blakey that the fraud challenge for banks ...

The funding round was led by Norwest, with participation S Capital VC, Cerca Partners, and Oceans Ventures. Snowflake Ventures also participated as a strategic investor.

They say a lot can happen in a week in politics. So imagine how much can change in a year within the world of AI! Even 12 months ago, Gen AI still wasn’t mainstream worldwide. But by the end of 2025, ...

Dynamic family data entry, deletion, and relationship querying using AIML + Neo4j + Streamlit. stage 2's Prolog/Python reasoning layer has been replaced with a Neo4j graph database. The AIML chat ...

Tracing product flow Analyzing supplier dependencies Tracking supplier risks and dependency chains Understanding APIs (Active Pharmaceutical Ingredient) dependencies and connections Identifying risks ...