Adding AI to an application is easy.
Building an AI platform that has to operate with real users, personal data, identity, recommendations, privacy requirements and European regulatory expectations is much harder.
This session uses nVibes, a European social platform, as a real-world case study for designing an AI-enabled platform on Microsoft Azure.
We will explore how application data, user context and AI services can be combined to create personalized experiences while maintaining clear boundaries around identity, privacy, security and responsible AI.
The architecture covers the journey from application data through contextual AI processing and agent-based workflows to user-facing recommendations.
We will also discuss an important architectural question:
How much context should an AI system actually know about a user?
Rather than presenting a theoretical reference architecture, I will show architectural decisions, trade-offs and lessons learned while building the platform.
A live demonstration will show the architecture working through the nVibes application and its AI-powered recommendation experience.
What attendees will learn
- How to structure an AI-enabled application architecture on Azure.
- How application data can become contextual input for AI systems.
- How agents and recommendation systems can interact with application services.
- How identity, security and privacy boundaries affect AI architecture.
- How Responsible AI principles influence actual engineering decisions.
- Which architectural trade-offs appear when AI reaches production.
Live Demo
User interaction → application context → AI → recommendation → resulting action inside nVibes
And then show the corresponding Azure architecture.