When data is scattered across dozens of services, traditional approaches don’t work. We’ll explain how we built an aggregated read model for over 30 million documents, handled 1,500 write RPS, and why Elasticsearch became a key component of the catalog.

In her talk, Irina will discuss how your mindset shifts when you move from “improving what already exists” to “creating something from scratch.” She’ll explore which roles are truly critical, where a team can be a strength, and where it can be a hindrance. She’ll also highlight which mistakes in team collaboration are the most costly in the early stages.

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.

Most companies start their AI transformation with technology, not a business problem — and end up stuck in endless pilots instead of real results. In this talk, I’ll share practical principles shaped by building and delivering 20+ AI strategies and 50+ solutions across industries — from retail to pharma — in the US, Europe, and Ukraine. We’ll cover the most expensive mistakes, what actually drives ROI, and why some AI projects scale while others remain just slides. Who is this for: • CEOs, business owners, and leaders who are implementing or planning AI • Tech leads and ML engineers who want their AI solutions to deliver real business impact

Description of the talk: - Background. Why we decided to add an assistant to the standard search. - Market Trends: How Agentic Commerce Is Evolving and Why It Matters Now. - Implementation: How we technically built and integrated the AI agent into Prom’s infrastructure. - MVP and experiment results. What the initial tests revealed and how users interacted with the assistant. - Key insights and conclusions. The main takeaways we identified during development and launch. - What's next. Plans for developing AI tools on the marketplace.

What happens when a large language model becomes the entry point to government services that operate under real-world load and in the context of an information war? In such an architecture, any request may be not only incorrect but also intentionally manipulative — and standard AI safety solutions prove far less reliable than they appear in laboratory benchmarks. In this talk, I will share how we built a custom guardrail module for Diia.AI after encountering the limitations of off-the-shelf filters and the high cost of the LLM-as-a-Judge approach. Instead of validating every request with a large model, we designed a cascade security architecture: fast ML classifiers filter out most of the traffic, while the LLM is invoked only where deeper contextual analysis is truly required. This talk is not about perfect models, but about trade-offs, constraints, and practical decisions that must be made when an AI system operates not on a laptop, but within a national-scale service.

This presentation is designed for engineers, architects, and technical leaders who want not only to use large language models, but also to understand how they work, how to interact with them through APIs, what challenges arise when building RAG systems, and how to solve them.

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.

With the rapid development of AI tools, the IT industry has reached a point where building a new project is often cheaper and easier than maintaining an existing one. However, for many large, long-running projects, rewriting everything from scratch is not the best option — especially if teams learn how to maintain and evolve them with the help of agentic tools. In this talk, we will explore real-world experience working with such projects, covering key aspects of collaboration with people, tools, technologies, and processes through practical case studies.

Most engineers eventually face the need to perform load testing: validating how a service scales, testing a new database, or running performance benchmarks for a new technology. At that point the obvious question arises — which tool should you use? Existing solutions work well for HTTP load testing, but they often become limiting when you need to test other protocols, model complex workload patterns (open vs closed systems, skewed distributions, hot partitions), or run distributed load testing in a cluster. In this talk, I will introduce NBomber — a load testing framework I created to address these challenges. We will cover: - why there was a need to build a new tool despite the existence of Gatling, Locust, and k6 - using .NET and F# to build latency-sensitive systems - the architecture of NBomber - how NBomber Cluster works - several practical use cases including database benchmarks, anomaly detection, Kubernetes integration, benchmark comparison, and performance trend analysis.