Implementing AI into a government service with 23+ million users is a journey of continuous product discoveries and challenges. In this talk, I will share the real-world experience of how the "Diia" ecosystem is transitioning from a classic Digital State (where users search for the required services themselves) to an Agentic State (where AI proactively fulfills the user's intent). What we will cover: - Product Discovery and Paradigm Shift: The transition from Digital State to Agentic State. Why traditional interfaces have reached their limits and how we validated the need for proactive AI solutions. - AI in Support as the First Big Step: How we automated 90% of requests without a drop in quality (CSAT). Soft AI UX: why people struggle with prompting and how we guide them using hybrid interfaces. - The Upskill Case and Team Transformation: We didn't fire a single operator. How we built internal AI tools for the team, turning yesterday's support agents into AI trainers. - Deep Dive into Diia.AI on the Portal: The launch of the world's first agentic service at the government level. How our RAG architecture works, how we architecturally protect personal data (PII) from entering the LLM, and how we repel jailbreak attempts.

During the talk, we will explore why a simple RAG approach is no longer sufficient for organizations with multiple data sources and how modern AI systems are evolving — from classic RAG to Deep Search, hybrid search, and Knowledge Graphs as a layer of corporate memory. We will also look at how to combine unstructured documents, tabular data, databases, internal wikis, chats, and business entities into a unified system where AI can find relevant sources, build a search path, explain relationships between facts, and provide more accurate and verifiable answers.

We'll explore the key challenges of developing RAG-systems for the Ukrainian language. We'll discuss evaluation methods for such systems in specific domain areas, like consulting citizens on public services, and uncover practical insights and common "gotchas" that arise during implementation.