Neo4j

The Connected Startup Episode 1: Papr Every startup is born to solve a problem. The hard part is everything that breaks on the way there. The Connected Startup is our new monthly podcast about those roadblocks, and the founders who discovered a graph was the way through. Each episode traces one company's path from the wall they hit to what they could finally build once their data matched the shape of the problem. We ask every guest the same question: describe the moment you realized a graph was the right tool. Our first guest is Shawkat Kabbara, founder and CEO of Papr, a context intelligence platform that gives AI agents long-term memory through a graph-native architecture built on Neo4j. Papr powers memory and retrieval for customers in fintech, commerce, healthcare, and insurance, turning unstructured data into queryable knowledge graphs via developer-friendly memory policies and a GraphQL API.

Neo4j Aura Agents won Best Innovation in AI Model Management and Deployment at the AI Tech Awards 2026. Thank you, DevNetwork! Recognition like this means a lot ❤️, and it belongs to everyone who helped build and shape Aura Agents. Congratulations to all the winners: https://lnkd.in/eTywQ4dz 📸 Yolande Poirier Leila Messallem, and Alex Thomas #Neo4j #AITechAwards #AuraAgents

Fraud is one network. So why fight it on three platforms? Detection catches it. Investigation rebuilds the context from scratch. Compliance writes the SAR by hand. Three systems, three silos, and the evidence that caught the fraud gets thrown away at every handoff. That's backwards. Fraud is one connected network. Fight it on one graph: the same graph that fires the 200ms alert is the one the analyst investigates, the SAR cites, and the next model learns from. Nothing thrown away. Every confirmed case sharpens the next. In production: +200% detection, 10ms latency, 2.2B relationships live. On 1 July, I'm running an EMEA webinar on the complete fraud lifecycle: detect, decide, alert, investigate, confirm, report, retrain. One graph behind every stage. 📅 1 July, 10:00 BST / 11:00 CEST Register: https://lnkd.in/gPBEtiub #FraudDetection #FinancialCrime #Neo4j

Aura Agent is a game changer: it grounds your AI in a Neo4j Knowledge Graph, helping stop hallucinations and getting more accurate #AI. Accuracy at Scale: Knowledge graphs map relationships among people, places, and things, serving as "pre-joined" data that tell your AI exactly how to navigate complex information. Rapid Deployment: Automatically draft, edit, and deploy a secure, working agent in just minutes using a low-code UI. Flexibility & Growth: Easily add new data sources—structured or unstructured—without the painful refactoring required by traditional relational databases. Enterprise Ready: Deploy via REST or MCP endpoints to any AI client, including Claude, with just a click. Whether you are managing complex supply chains or optimizing HR talent distribution, Aura Agent delivers the performance and flexibility to pivot quickly in the world of AI. Zach Blumenfeld’s Full video + demo: https://lnkd.in/dGHevNvS - get started today!

💉 💊 The 3rd edition of GraphTalk Pharma and Life Sciences is coming to Munich on July 2, 2026, and you have the chance to attend in person AND online! The agenda follows the full pharma product life cycle, from R&D to clinical to supply chain, with real-world use cases from teams already running knowledge graphs and context graphs in production. The agenda is stunning: -Vladislav Kim from Bayer Pharma R&D on knowledge graph reasoning and GraphRAG for early target discovery. -Bjoern Oest Hansen from KWS Saat on integrating knowledge graphs and AI for explainable candidate gene discovery. -Brent Murphy from BASF SE on using knowledge graphs to consolidate biological context for non-model plants. -Dr. Olga Endrich, MD, and Dr. Karen Triep from Insel Gruppe AG on mapping Swiss medication and procedure data in Neo4j. -Dr. Peeyush Sahu from Sandoz on using graph to reveal evergreening patterns in patents. -Mambwe M. from Boehringer Ingelheim on building an agentic layer over a pharma supply chain context graph. -Jonathan W. Lowe from Amalgo LLC on the financial returns of reducing QA investigation durations with AI and context graphs. Plus a technical deep dive from Jean-Marc Guerin and Niels de Jong on context graphs and agentic intelligence, and an opening keynote from Dr. Dr. Alexander Jarasch, Neo4j's Global Head of Pharma and Life Sciences. If you are working in pharma or life sciences and want to see what graph intelligence looks like in production, this is the event to be at. You are on time to register - don't miss it and spread the word!: https://bit.ly/4xAGUV0 #PharmaAI #LifeSciences #KnowledgeGraph #ContextGraphs #GraphRAG

Get started on #Neo4j: ✔️ Start for Free ✔️Deploy via Cloud Marketplaces ✔️Scale on Demand ✔️Import Your Data Fast What's the best plan for you? ⬇️ (special offer for #startups - check it out: https://bit.ly/4xwg5S5

⛓️💥A shipment is late. A supplier is struggling. Demand just spiked in a region you were not expecting. And by the time your system surfaces an alert, the damage to your #supplychain is already moving downstream through production, logistics, and fulfillment, one cascading failure at a time. The problem is not the data but the lack of connection of the data: when something breaks, you are tracing dependencies manually, making decisions with an incomplete picture, and reacting to events that a better-connected system would have surfaced hours or days earlier. A #GraphDatabase changes the model entirely. How? with a living network of suppliers, orders, shipments, routes, and relationships that mirrors how your supply chain actually works. You can ask questions like "if this supplier fails, what are the downstream impacts?" and get an answer in seconds, not weeks. BASF the world's largest chemical producer, used this approach to analyze upstream and downstream dependencies across its global value chain. What previously took weeks of analysis now takes seconds. That shift, from reactive alerts to connected intelligence, is what supply chain resilience actually looks like in practice. We put together a practical guide to building it. Link in the comments. #Neo4j #SupplyChain #GraphIntelligence #KnowledgeGraph

Attending Neo4j GraphTalk Pharma on 2 July in Munich? It's worth noting that the 2nd Edition of GraphTalk Manufacturing is taking place at the same time and location at Design Offices München Nove. One session that may be of particular interest is Siemens' presentation, "From Unstructured Fragments to Portfolio Intelligence at Siemens", where Michael Derfler share how they use Neo4j for entity resolution to create trusted, connected data foundations that improve visibility, reduce duplication, and support smarter decision-making across manufacturing environments. If you're joining us in Munich, this is another great opportunity to hear how graph technology is delivering real-world business value across industries. https://lnkd.in/dXqy6RRT

On 7 July we (Neo4j) are hosting GraphTalk London: Financial Crime at One Hundred Shoreditch, in partnership with Capgemini. It's a small, senior morning for banking and financial services professionals. We'll be looking at how AI is changing the financial crime threat, and how graph technology can help organisations move from reactive detection to getting ahead of it. Some great speakers lined up, including industry expert Colin Whitmore - Financial Crime - AI, Strategy and Innovation and Chris Booth from our team at Neo4j, alongside Alister Coates from Capgemini. If this is relevant to you or someone in your network, seats are limited and registration is open. Sign up and see more here 👉 https://lnkd.in/eM74xqzj #FinancialCrime #GraphTechnology #AI #Neo4j #Capgemini Morten Schlosser Joakim Nilsson Faye Turpin (Ashman)

Use Neocarta to easily build semantic layer graphs for your agentic applications in #Neo4j Alexander Gilmore shows how to build a semantic layer on #GCP using NeoСarta, a Neo4j Labs library that turns your data systems into a traversable graph. How? Instead of hardcoding schema context into prompts, agents traverse from an entity to the right LookML view, confirm the glossary term, check lineage, and then issue the query. All in one traversal. Worth reading if you are building agents on Google Cloud and hitting the limits of flat schema files: https://bit.ly/4vepyvP