Arango

To wrap up our incredible lineup for GraphCon, we have one of the most exciting intersections in modern tech: the marriage of Graphs and AI Agents. Let's welcome Emmet B. Allen, AI Solutions Engineer at Arango to GraphCon! If you've been following the graph infrastructure space, you know Arango has been doing incredible work pushing the boundaries of multi-model graphs for ages. Emmet is on the front lines, helping organizations bridge traditional graph data representations and intelligent AI orchestration. He'll be joining us in Seattle on July 25th to deliver a fantastic, forward-looking session: 🎙️ "Agentic Knowledge Graphs: Leveraging Agent Orchestration and Graphs" We talk a lot about how graphs can feed AI models, but Emmet will also explore what happens when the agents themselves power and orchestrate knowledge graphs. He'll look at the intersecting ecosystems, and how AI workflows and rich graph contexts can constantly reinforce one another. 📅 This is our last speaker announcement, so check out the full list and secure your spot. https://lnkd.in/gxEeEhCm

Most enterprise AI teams are still fighting to make answers accurate and grounded. Many still haven't won that fight. But even once you do, your AI can only answer the questions someone already knew to ask. That presents an interesting challenge. 🔍 That's the gap Contextus closes. 🧩 A graph-guided discovery agent on Arango's Contextual Data Platform, Contextus trains a Graph Neural Network (GNN) on your graph's structure, then uses link prediction to surface nodes that are structurally similar even when they sit in completely separate neighborhoods. A reasoning agent explains each discovery, including the connection path, the predicted link strength, and what it means in your domain's language. And because it's Arango, the GNN embeddings live in the same collection as the node attributes and the graph itself. No bolt-on vector database. No ETL. No sync layer. Training takes minutes. ⚡ The proof? 📊 On a 27,583-node chip-design graph spanning four separate RISC-V repos, Contextus surfaced near-identical memory management unit architectures hidden across repos — invisible to keyword search and to a direct AQL query. D.B. Morris breaks down the full engineering architecture in our latest technical deep dive 👉 https://lnkd.in/eR3gKHne Want to see it in action and ask questions live? Join Daniel at our Agentic AI Context Builders meetup on July 9: https://lnkd.in/eXTkK8jJ #GraphAI #BusinessContext #DataArchitecture #RAG #KnowledgeGraph #AIDiscovery #Arango

📢 Join us live on July 16th. As organizations expand their AI initiatives, many are discovering that building reliable, explainable AI at enterprise scale starts with the right data architecture. Hear directly from Ravi Marwaha, Chief Operating Officer & Chief Technology Product Officer, and Mark Milinkovich, Director of Product Marketing, as they share a practical framework for designing a modern contextual data layer that combines real-time enterprise knowledge, governance, and connected data to support intelligent decision-making and action. We'll walk through the 6 architectural requirements for building a modern contextual data layer, including: ✅ Semantic clarity ✅ Graph-native relationships ✅ Temporal awareness ✅ Provenance and trust ✅ AI-native services ✅ Unified multimodel data 👉 Register today: https://lnkd.in/epjVPjTr #Webinar #Arango #EnterpriseAI #AIArchitecture #ConnectedData

Your data already contains the answer. The problem is that no dashboard, search bar, or AI assistant was built to find it — because the answer lives in the connections between things, not the things themselves. Traditional search and standard SQL queries are great at finding records. They're far less effective at uncovering how those records connect. And when you can't see the connections, you're missing the context that drives better decisions. Join us on Thursday, July 9, for a live, hands-on session with Mark Milinkovich and D.B. Morris to explore how graph databases and connected data help AI systems uncover answers, context, and insights that traditional search and retrieval methods often miss—and learn practical ways to break down these limitations. We’re pulling back the curtain on Contextus, a discovery agent built on the Arango Contextual Data Platform. It bridges the gap between deep graph relationships and smart reasoning to surface high-value insights that would normally take a team of human analysts days to uncover. What we’ll cover: 1️⃣ Beyond the Flat Query: How a flexible data architecture naturally stores and navigates deeply connected data. 2️⃣ Instant Clarity: How Arango AutoGraph automatically turns raw enterprise chaos into a structured knowledge graph. 3️⃣ Real-World Execution: How grounding smart tools in structured knowledge makes them genuinely accurate, reliable, and practical for business decisions. If you’ve ever wondered what graph databases actually unlock that traditional relational databases can’t, this is the session you can't miss. No abstract theories just a practical look at how connected data works under the hood. 👉 Secure your spot: https://lnkd.in/eXTkK8jJ

Are your production AI agents actually understanding how your business works, or are they just delivering generic answers? 👀 When we try to scale enterprise AI, traditional data infrastructure hits a wall. We stitch together separate point solutions for vectors, graphs, documents, key-value and search. The result? A "Frankenstack" that tries to reconstruct business relationships on every single inference call. 💡 It leads to skyrocketing architectural overhead, synchronization lags, and fragmented governance. To build production-grade, reliable AI, you need a Live Contextual Data Layer built on a multimodel graph database that connects structured, semi-structured, and unstructured data into a single, trusted representation of your business. Curious about how to build one? 👉 Check out our new eBook:  https://lnkd.in/eXh9NpEF It breaks down the 6 key architectural requirements to ensure your data is retrieval-ready before the first query is run enabling both VectorRAG and GraphRAG without the pipeline mess. Arango #DataPlatform #GenerativeAI #KnowledgeGraph #VectorSearch #TechArchitecture #Arango

Why do enterprise AI agents thrive in testing, but consistently fail in production?  A complete lack of live, temporally aware business context. When an AI agent doesn't know what happened five minutes ago across your enterprise systems, it simply cannot reason or make accurate business decisions. I recently spoke on the "AI in Business" podcast by Emerj Artificial Intelligence Research to unpack exactly how we solve this bottleneck. My core argument is simple: leaders need to stop treating business context as a simple data pipeline problem and start treating it as fundamental infrastructure. At Arango, this is precisely the shift we are driving moving away from static data dumps toward dynamic, a live contextual data layer with a multimodel graph database at the core. For CIOs and CDOs looking to bridge this gap, I broke down 5 actionable frameworks to architect these real-time, explainable context layers on top of your current systems completely bypassing the need for a painful, expensive "rip and replace." We dove deep into real-world applications where this infrastructure is making an impact right now: ➡️ High-tech semiconductor engineering: Navigating hyper-complex, shifting data environments. ➡️ Clinical trial site selection: Accelerating timelines with real-time variables. ➡️ Enterprise customer support: Moving past rigid scripts to actual, dynamic reasoning. If you are trying to turn raw AI potential into measurable enterprise performance, this conversation is for you. 💡 🎧 Tune in to the full episode here: https://lnkd.in/g6dx3fHm Let me know your thoughts in the comments, how is your organization tackling the contextual data layer challenge? #EnterpriseAI #ContextInfrastructure #AIAgents #DataStrategy #Arango #EmerjPodcast

🏆 June “Rising Star” Award: Emmet B. Allen 🏆 At Arango, we love recognizing team members who embrace new challenges, grow quickly, and make a meaningful impact from the very beginning. Emmet exemplifies what it means to be a Rising Star. Through his proactive attitude, eagerness to learn, and ability to navigate new challenges with confidence, he has quickly become a trusted contributor on the team. In particular, Emmet's rapid growth and expertise in our AI Services have stood out, earning him recognition from colleagues across the company. Thank you, Emmet, for your dedication, growth mindset, and the impact you're already making. This recognition is incredibly well deserved, and we're excited to see what you'll accomplish next! 👏 Interested in joining a team that celebrates growth, learning, and impact? We're hiring! Check out our careers page to learn more: https://arango.ai/careers/ #EmployeeRecognition #RisingStar #GrowthMindset #Teamwork #AI #Arango #Hiring

Is your data ready for the era of Agentic AI? 🤖📊 As AI evolves from passive chatbots to autonomous, goal-oriented agents, a massive shift is happening. But here is the reality: Agentic AI is only as powerful as the data infrastructure supporting it. If your organization's data is trapped in silos or lacks business context, your AI agents won't be able to reason, make decisions, or deliver real business value. Leaders must act now to bridge the gap between raw data and agentic AI ready-data. Our very own CEO Shekhar Iyer recently broke down this exact challenge for Forbes Technology Council, sharing critical insights on how forward-thinking leaders can architect their data ecosystem to be Agentic AI-ready. Read the full article here: 👇 🔗 https://lnkd.in/gCbgK-sT Arango #AgenticAI #DataStrategy #Forbes #TechLeadership #forbestechnologycouncil #DataInfrastructure #Arango

📊 Delivering pricing intelligence is one thing. Delivering it in real time at scale is another. As Matpriskollen's data volumes and customer expectations grew, they needed a simpler, faster foundation for turning pricing and promotion data into actionable insights. It has been incredibly rewarding to watch Fredrik Mazur and the wonderful team at Matpriskollen tackle this challenge head-on. What started as a pricing intelligence platform for Swedish consumers has evolved into a critical source of pricing and promotion insights for retailers, suppliers, and brands across the market. Before partnering with Arango, delivering those insights required a complex setup of multiple databases, data pipelines, and BI layers. As data volumes and customer expectations grew, the need for a simpler and more scalable data foundation became clear. By building on a contextual data layer that brings together connected data, search, and AI-ready capabilities, Matpriskollen was able to simplify its architecture while delivering faster, real-time insights at scale. Today, Matpriskollen is turning massive volumes of pricing and promotion data into real-time intelligence. By bringing graph, document, and vector search together in a single platform, they can: ✅ Deliver real-time answers instead of relying on outdated reports ✅ Replace multiple databases, pipelines, and analytics layers with a single data foundation ✅ Access insights 10–100× faster than before ✅ Scale with growing data and query volumes without increasing complexity ✅ Provide retailers, suppliers, and consumers with up-to-date pricing and promotion intelligence The result is a faster, simpler, and more scalable platform that helps Matpriskollen deliver the real-time intelligence their customers need to stay ahead. We're proud to be part of their journey and grateful for the opportunity to collaborate with such an innovative team. A huge thank you to Fredrik Mazur and everyone at Matpriskollen for your trust and partnership! 🙌 📖 Read the full customer story: https://lnkd.in/g-YG7Skz #RetailTech #PricingIntelligence #DataAnalytics #GraphTechnology #CustomerSuccess #Arango