Discover how the biggest e-commerce company in Turkey, used LLMs and automation workflows to boost engineering speed by 30%–improving tests, documentation, and delivery, while keeping production stable, developers confident, and AI adoption practical, measurable.
Berlin • November 9 & 10, 2026
AI. Burnout. Big decisions.
The pressure is real. Berlin is where you work through it.
Every engineering leader wants AI-driven productivity–but few achieve it without chaos. In this talk, I’ll share how our teams integrated LLMs and automation tools like n8n, Cursor, A2A, and MCPs into a 2,000-engineer organization to deliver a 30% improvement in delivery speed while keeping production stable.
This isn’t a story about hype or magic prompts–it’s about engineering discipline, governance, and workflow design. You’ll see how we automated testing, documentation, and code reviews; built smart pipelines; and measured impact in ways that developers trusted. I’ll also share what didn’t work–over-automation, misaligned incentives, and cultural resistance–and how we overcame them.
Attendees will leave with practical strategies to safely scale AI adoption, boost team productivity, and blend human creativity with machine efficiency–all without breaking the systems or the culture that make great engineering teams thrive.
Key takeaways
Whether you’re AI-curious or already experimenting, you’ll leave with a playbook for:
- Learn how to integrate LLMs and automation without disrupting production stability.
- Discover practical methods to boost engineering productivity by 30%.
- See real examples of AI-driven testing, documentation, and code reviews.
- Understand how to build trust and governance for safe AI adoption.
- Take home a proven framework for scaling AI across large engineering teams.
