Artificial Intelligence is no longer confined to digital spaces - it is rapidly expanding into the physical world, transforming how machines perceive, move, and interact with their environments. One may say that Robotics is the next frontier for AI to solve. You will get a perspective on developing in the field from a research engineer who’s been in the field near a decade and from an ML engineer who switched to the field just two years ago. We will talk about what Physical AI is, how it is described, advertised and what tasks it tries to solve. But also we will talk about how brittle and fragile things in the real world are and what unique challenges merging AI and robotics pose.

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.

Let’s be honest: in most teams, performance optimization only becomes a priority when something is already on fire. And even then, it’s usually handled by one or two people. Not because others don’t care — but because real optimization requires a lot of time, context, and expertise. At Temabit, we decided to experiment with a different approach: delegating part of the optimization work to agents. In this talk, I’ll share what came out of it: how we learned to frame optimization tasks so agents can produce useful results, how we validate their suggestions, and why optimization turned out to be much harder to delegate than writing code — but potentially far more valuable. Real cases, honest lessons, no hype. And one key question: how realistic is it to trust agents with the performance of your product?

AI development hits everyone, so hit us. Everybody wants AI agents to replace regular UIs. In this talk, I will describe the evolution of our multitool AI agent, built with Node.js on top of Google Vertex AI. I’ll dive into our journey of choosing the right models and scaling development through CI/CD, TDD, and performance monitoring. Is it even possible to achieve stable results for AI projects that can hallucinate and return various responses? Interestingly, we eventually decided to remove MCP servers and Zod schema validation—technologies often considered the "standard" for these tasks. Want to know why we moved away from them? Join my session to get these insights and ask your questions live!