Latency is the time delay between when a caller finishes speaking and when the AI agent begins its response. Low latency creates natural conversation flow. High latency creates awkward pauses that make interactions feel robotic.
What contributes to latency?
Total latency includes speech recognition processing, network transit to AI services, language model inference time, and text-to-speech generation. Each component adds delay. Optimization requires attention to all parts of the pipeline, from audio streaming to model selection to infrastructure placement.
Why does latency matter?
Human conversations have a natural rhythm, and people expect a reply within a fraction of a second. A longer pause makes the system seem slow, and callers may think they were not understood and repeat themselves, which disrupts the flow further. Voice AI needs low latency to feel conversational.
Latency in practice
Callers complain that an agent pauses too long before answering. The team times each stage of the pipeline to see how much of the delay comes from speech recognition, language model inference, and text-to-speech. Streaming responses, choosing a faster model, and moving infrastructure closer to callers shorten the pause until the conversation feels natural.