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How to quickly build LLM-powered apps through an AI-ready architecture
Go from prototype to production with LLM apps that integrate, scale, and adapt as your AI and data stacks evolve.
Overview
LLMs are powerful—but building usable, adaptable apps with them is still a challenge. This session shows how teams are applying modern integration architecture to bring LLM-powered apps to production faster, with less risk and more flexibility.
What you’ll learn
How to turn LLM prototypes into production-ready apps without rewriting your stack
What makes an integration platform “AI-ready”—and how it helps you move faster
How to design for change with composable architecture and fast-evolving models
How to manage vector storage, orchestration, and governance across evolving LLM workloads
Session chapters
Why LLM apps stall
Common blockers: Integration, data, and risk
Modern architecture for AI-infused apps
How composable integration accelerates development
What makes Tray an AI-ready iPaaS
Real-world use cases and design patterns
Next steps: Where to go from here
Featuring
Paul Turner
Automation Expert
