How Tavus used Qdrant Edge to create conversational AI

Tavus, a human-computer research lab, developed the Conversational Video Interface (CVI) to deliver natural, human-like interactions by reading tone, gesture, and on-screen context in real-time. To address the challenge of maintaining subsecond conversational flow, Tavus used Qdrant Edge for fast, local data retrieval, eliminating network latency by implementing per-conversation edge vector stores. This design allowed for immediate data processing, avoiding serialization delays and focusing on retrieval quality and multimodal accuracy. By reducing retrieval time to 20-25ms, Tavus maintained timely, accurate responses and enhanced the user experience, even for complex conversations. The architecture improved operational efficiency, with Tavus indexing millions of data points and providing a seamless launch experience without the need for customers to build their own Retrieval-Augmented Generation (RAG) systems. This approach demonstrated the effectiveness of architecture over micro-optimizations, enabling Tavus to prioritize quality and safety in their AI system.