Imagine one day your team inherits a system that was built over 4 years by six different teams — just to test hypotheses. No documentation. Just a massive monorepo and Jenkins for deployment. That’s exactly what we were handed, and that’s when we decided to take full inventory — starting with Architecture as Code. In this talk, I’ll share how we approached architectural documentation systematically: from building C4 diagrams to creating Service Documentation, ERDs, and Sequence Diagrams. You’ll learn how we practically restored our understanding of the system, brought architectural clarity, and which tools (PlantUML, Mermaid) and methods worked best. This talk isn't just about diagrams — it's about surviving chaos, synchronizing a team, and driving architectural evolution through transparency.

What can go wrong if you allow each service to access the database directly? In a startup, this seems like a quick and easy solution, but as the system scales, problems appear that no one could have guessed. In my talk, I'll share Solidgate's experience in transforming its architecture: from the chaos of direct connections to a service-based data access model. I will talk about the transition stages, bottlenecks, and how isolation affected infrastructure support. I will honestly show what worked and what didn't. In short, we will analyze the controversy of this talk.

When human safety is at stake, technical reliability is not just a requirement. In this talk, we'll look at architecture, workloads, WebSocket solutions, Kubernetes scaling, and other technical aspects of creating an airborne alarm map. This is a story not only about code, but also about responsibility.

Let's talk about our history. How we started the project with a small vector database of less than 2 million records. Later, we received a request for +100 million records, then another +100... And so gradually we reached almost 1 billion. Standard tools were quickly running out of steam - we were running into performance, index size, and very limited resources. After a long series of trials and errors, we built our own low-cost cluster, which today stably processes thousands of queries to more than 1B vectors.