Program Manager - Compliance Operations & Process Excellence

Nvidia GTC 2026: Arango invites AI leaders and builders to explore the Contextual Data layer powering production AI Agents. Visit Booth 3421 to see how Arango's out-of-the-box contextual data layer enables AI agents to reason, decide, and act using unified, current, and trusted business context at enterprise scale. Arango, announced it is participating in Nvidia GTC 2026, taking place March 16-19 at the San Jose Convention Center.AI leaders and builders attending the event are invited to visit Booth 3421 to meet Arango executives and contextual data experts, explore demonstrations, and see how organizations are using a contextual data layer to power AI agents that reason, decide, and act across enterprise data. The Business Context Gap in Enterprise AI As enterprises race to deploy AI agents that can reason, decide, and act across complex workflows, many are discovering that the biggest barrier isn't the model - it's the lack of business context connecting fragmented enterprise data. At Nvidia GTC 2026, Arango will demonstrate how organizations are closing this gap by building a contextual data layer that enables AI systems to understand what things mean, how they relate, when they were true, and where they came from - creating a foundation where organizations define business context once and reuse it across AI agents, applications, and workflows. Arango is trusted by leading organizations, including Nvidia, HPE, Linx Security, the US Air Force, and Articul8 to power mission-critical workload Why Business Context, Trust and Scale Matter for Enterprise AI Agents Nvidia GTC AI Conference brings together the global AI ecosystem to explore the latest advances in AI. From Jensen Huang's keynote on the future of accelerated computing and AI to technical sessions focused on building intelligent systems that can reason, plan, and act, the conference highlights the technologies shaping the next wave of enterprise AI. As organizations move from experimentation to deploying AI agents in production, many encounter a critical gap in the AI stack: the absence of a unified business context layer that connects fragmented enterprise data. Industry analysts increasingly highlight the importance of unified multimodel data platforms that combine graph, vector, document, and other data types to support advanced AI workloads and reasoning systems. For example, The Forrester Multimodel Data Platforms Landscape, Q4 2025 highlights the growing role of multimodel data architectures in helping organizations manage complex, interconnected data required for AI systems that enterprises can trust to reason, decide, and act. Real-World Applications for Context-Driven AI Organizations are increasingly recognizing the need for a contextual data layer to power AI agents across complex enterprise workflows. Examples include: * AI support agents for engineers diagnosing issues across products, systems, and documentation * Engineering and semiconductor design intelligence where AI agents trace chip designs and product changes across development, testing, and production systems * Customer service co-pilots guiding frontline staff with real-time operational context * Cybersecurity investigations where AI agents analyze relationships between alerts, systems, and vulnerabilities to identify root causes and remediation steps * Clinical trial intelligence where AI agents identify high-performing trial sites by reasoning across investigators, institutions, protocols, and outcomes * Supply chain intelligence and digital twins where agents reason across suppliers, logistics networks, and operational systems * Video intelligence and incident response where AI agents reason over live video streams and enterprise data to deliver instant, explainable insights Many organizations attempt to assemble this contextual layer themselves - starting with vector search for retrieval, then adding graph technologies for relationships, document stores for content, and pipelines to connect everything together. The result is a complex, fragile, and fragmented stack that becomes difficult to maintain as AI initiatives scale. Arango delivers this contextual data layer out of the box, allowing teams to power AI systems with unified business context without stitching together multiple databases and pipelines. See Context-Driven AI in Action at Booth 3421 At Booth 3421, Arango, a member of the Nvidia Inception Program, will showcase live demonstrations of the Arango Contextual Data Platform, illustrating how organizations empower AI agents with unified, current, and trusted business context at scale through a simplified, agentic-AI-ready data architecture delivered out of the box. Live Demonstrations * Integrated Circuit Design Knowledge Graph Trace chip designs from development through production support. AI agents reason across engineering data, documentation, and system records to accelerate troubleshooting and design decisions * Infrastructure Security: Root Cause and Remediation Identify failures, configuration changes, root causes, and remediation steps by connecting alerts, logs, and documentation into a unified operational context * Nvidia Video Search and Summarization + Arango GraphRAG Turn hours of video into actionable insights by connecting video evidence with enterprise data, enabling AI agents or humans to detect events, identify patterns, and accelerate operational decisions. Nvidia will also be featuring version 3.0 of this blueprint at their booth * Arango Contextual Data Platform 4.0 New Features Arango will unveil the Arango Contextual Data Platform 4.0 and highlight key feature demos on March 17, 2026 during Nvidia GTC "Enterprises are deploying AI agents that must reason, decide, and act across complex business environments - but without the right business context across enterprise data, they cannot succeed," said Shekhar Iyer, CEO, Arango. "At Nvidia GTC, we're excited to meet with AI leaders and builders and demonstrate how a contextual data layer helps make AI systems reliable and production-ready."

Arango has launched Contextual Data Platform 4.0 at NVIDIA GTC, designed to help organisations build enterprise AI agents and applications more reliably. The platform introduces a Contextual Data Layer that transforms fragmented enterprise data into unified business context for AI systems. The release includes the Agentic AI Suite with over 20 built-in AI services, AutoGraph for automated context graph generation, AutoRAG for optimising retrieval, and Ada, an AI digital assistant. The platform supports deployment across cloud, on-premises and hybrid environments. Early adopters report 30–50% reduction in integration complexity and 2–4× faster AI development cycles. Customers include PSI CRO, Matpriskollen, Transient.AI and Linx Security. The platform also supports Bring Your Own Code/Container deployment, allowing organisations to integrate preferred models whilst maintaining security and compliance control.

Transient.AI, an AI platform for capital markets, has selected Arango's AI Data Platform as its data infrastructure to deliver explainable, real-time intelligence for hedge funds, asset managers and investment banks. The partnership enables Transient.AI to model complex financial relationships whilst maintaining transparency in AI-driven outcomes. The New York-based company, founded in 2024, required a unified system capable of combining graph, vector and document data whilst supporting real-time analysis of interconnected entities including strategies, instruments and risk factors. Arango's multi-model platform eliminates data fragmentation across AI workflows, enabling explainable decisions at institutional scale. The infrastructure supports front-office workflows including institutional sales, trading and research by delivering AI-driven recommendations on counterparty engagement and market opportunities. Financial institutions face mounting regulatory pressure to ensure AI decisions are both accurate and auditable.