How Tavus used Qdrant Edge to create conversational AI - Qdrant

This post details how Tavus implemented per-conversation edge collections using Qdrant Edge to achieve sub-second conversational AI. The core technical contribution is the architectural decision to colocate Qdrant instances with conversational workers, eliminating network hops for embedding and ANN lookups. This design choice prioritized retrieval quality and multimodal accuracy over micro-optimizations like quantization, enabling retrieval on every utterance and speculative execution. The result was a reduction in retrieval latency to 20-25ms at the edge, contributing to an end-to-end utterance-to-utterance timing of 500-600ms, which feels natural to users. The post highlights the business impact of this architectural choice, including simplified onboarding for customers and improved developer velocity.