Most AI initiatives fail due to fragmented data, lack of context, and growing compliance risk. Join an exclusive, invite-only session with Neo4j, Reply, and AWS to learn how leading organisations are solving it.
As GenAI adoption accelerates across EU institutions and regulated industries, most organisations are still stuck in pilot stage.
The reason is simple: AI that isn’t grounded in connected, trusted data cannot be relied on.
The result? Inaccurate outputs, limited explainability, and increasing compliance risk.
In this session, you’ll see how organisations such as Sogei and Porsche are transforming fragmented, siloed data into context-rich, AI-ready foundations — enabling GenAI that is accurate, explainable, and production-ready.
With increasing pressure from the EU AI Act and industry regulators, organisations must now move from experimentation to operational, trusted AI — while addressing both data integrity and skills readiness.
Discover how context graphs make this possible, grounding AI in connected enterprise data to deliver reliable, transparent outcomes in real-world environments.
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Why Attend:Â
- See how organisations are moving from AI pilots to production-ready, trusted systems
- Understand how context graphs reduce hallucinations and improve decision confidence
- Learn how to ground LLMs in connected, real-world data
- Discover how to design AI aligned with compliance, sovereignty, and resilience requirements
- Explore real-world use cases from Porsche and Sogei
- Gain practical insights into scaling AI across complex, regulated environments
Who Should Attend:
Senior leaders and practitioners across EU institutions and regulated industries, including:
Financial services (banking, insurance)
Energy & utilities
Telecommunications
Healthcare & pharma- IT Decision Makers & Programme Leaders
- Data & Enterprise ArchitectsÂ
- Heads of Digital Strategy & Innovation
- AI, Data & Transformation LeadersÂ
Sogei | Detecting Supply Chain Fraud and Ensuring Compliance across Complex, Regulated Data Ecosystems
Barbara Damiani, Project Manager, Sogei, Alessia Varriale, Project Manager, Sogei and Alessio Mazzola, Manager, Whitehall Reply
This presentation explores the evolution of supply chains from simple chains to complex networks, using data from import process and Neo4j Graph Data Science to uncover hidden patterns and anomalies. By analyzing real-world cases of logistics disruptions and fraud, we demonstrate how intelligent alerts and LLM integration can transform complex graph data into clear, actionable insights.
Porsche | Transforming Complexity into Business Insight
Peter Pfeiffer, Product Manager, Porsche and Lars Funke, Senior Manager, KI Reply
Porsche is transforming intricate legacy systems into a living, connected knowledge graph—bringing visibility to previously unseen dependencies across its technology landscape.
Discover how this approach enables faster root cause analysis, supports AI-assisted modernisation, and empowers teams to make quicker, lower-risk decisions in highly dynamic environments.
AWS | Laying a Strong, Secure Data Foundation to deliver Trusted Agentic AI
Matt Eglin, Solutions Architect, AWS
As organisations move from AI experimentation to real-world implementation, ensuring accuracy, transparency, and compliance becomes critical. This is particularly relevant with the evolution of AI agents, making it possible to automate complex business workflows. Maintaining trust in these agents is crucial for their adoption.
Through a real-world financial services example, we'll showcase how agentic AI is being used to transform and enhance processes in complex, regulated environments.
