The Retrieval Layer for Edge AI

Edge AI is almost there: on smart glasses, robots, phones, kiosks… The last push it needs is semantic search over local data that still works when connectivity is weak, and when latency and privacy are non-negotiable. On-device vector search unlocks “memory” and real-time assistance across devices: from “help me find my keys” on smart glasses, to robot task context, to anomaly logging in vertical farming, to offline support and personalization on phones and kiosks. In this talk, I’ll show how we made that possible with Qdrant Edge: an embedded vector search engine built for the constraints of edge devices. I’ll walk through the architecture and core engineering tradeoffs (storage layout, incremental updates, index lifecycle), and tie it together with a demo (smart glasses are mentioned here for a reason!).