Dat Tran
Dat is a seasoned technology and business leader with deep expertise in AI, machine learning, and digital transformation. As Partner & CTO at DATANOMIQ, he advises companies on AI strategy and implementation. Before that he worked for Axel Springer SE, idealo.de, Pivotal Labs and Accenture. His interests are diverse from traditional machine learning, deep learning, computer vision, AI in general to large language models. He has a lot of experiences from devising realistic data-driven use cases to the actual implementation into a real product; more than capable of distinguishing hype, buzzwords and wannabes from substance. He’s actively engaged with the global tech community, sharing insights on AI, tech leadership, and digital transformation with over 76k+ followers on LinkedIn. As a frequent keynote speaker, he has presented at conferences such as PyData, WeAreDevelopers, and many more, mentoring professionals in machine learning and leadership along the way.
Session
In aviation, search isn’t simple—people use abbreviations, slang, and technical terms that make exact matching tricky. We started with just Postgres, aiming for something that worked. Over time, we upgraded: semantic embeddings, reranking. We tackled filter complexity, slow index builds, and embedding updates and much more. Along the way, we learned a lot about making AI search fast, accurate, and actually usable for our users. It’s been a journey—full of turbulence, but worth the landing.