As enterprises race to harness GenAI's transformative power, they're caught in a critical dilemma: innovate rapidly or sacrifice control? Commercial AI Model Providers offer convenience but demand a steep price—your data sovereignty.

What if you could have both cutting-edge capabilities and complete control?

In this session, we'll share our practical experiences building an enterprise-grade GenAI platform entirely with open-source components. We'll walk through our OSS architecture that integrates model serving (KServe, vLLM etc), orchestration tools, agentic frameworks, and no-code/low-code interfaces—delivering capabilities that meet enterprise needs while keeping sensitive data firmly behind your firewall.

Through real-world implementation example, we'll discuss what worked, what didn't, and the lessons learned along the way. You'll see how we addressed scaling, compliance, and performance challenges—all while maintaining full control of data and AI infrastructure. Our goal is to contribute these insights back to the community to help advance open-source enterprise AI adoption.