Real-time AI refers to AI systems that respond fast enough to maintain the natural flow of human conversation — typically within 500 milliseconds from the end of speech to the start of audio playback. Below this threshold, conversations feel live; above it, they feel like waiting.
Try Lucy OS1 →Real-time performance in voice AI requires optimising every step of the pipeline: microphone capture, STT streaming, LLM inference, TTS streaming, and audio delivery. Each stage adds latency, and the total budget for a natural conversation is under 500ms. Achieving this requires specialist infrastructure — not just the models, but how they are orchestrated and pipelined together.
Lucy OS1 is built around a real-time WebSocket pipeline. Deepgram streams transcription as you speak, GPT-4o-mini begins inference before you finish talking, and Cartesia streams audio back in chunks. The result is a conversation that feels live, not like waiting for a webpage to load.
Try Lucy OS1 →Returns partial transcripts in real time as you speak rather than waiting for a complete sentence, allowing the LLM to begin processing before speech ends.
Large language models generate tokens one at a time. Streaming those tokens to the TTS as they arrive rather than waiting for the full response halves perceived latency.
The first audio chunk plays before the full response is synthesised. Users hear the first word within 200ms while the rest is still being generated.
HTTP request-response adds overhead for every turn. Real-time AI uses persistent WebSocket connections that stay open throughout the conversation, eliminating connection setup latency.
What is an acceptable AI response latency?
Humans perceive gaps above 300ms in conversation. Under 500ms is comfortable; under 300ms feels live. Above 1 second starts to break conversational flow noticeably.
Why is most AI so slow?
Most consumer AI products use HTTP APIs designed for batch use, not real-time conversation. Each round trip adds 100-300ms overhead. Streaming and WebSocket architectures eliminate this.
Does real-time AI sacrifice accuracy for speed?
Not necessarily. The major LLMs (GPT-4o, Claude) have streaming inference that delivers the same quality output as batch — just faster. Specialised faster models (like GPT-4o-mini) make small accuracy trade-offs for significantly lower latency.
Can real-time AI work on a slow internet connection?
STT and TTS both require real-time audio streaming, which needs a stable connection with low jitter. 4G LTE or Wi-Fi works well. Satellite or very congested networks may cause noticeable degradation.
Lucy OS1 puts these concepts to work in a real, streaming voice AI pipeline — Deepgram STT, GPT-4o-mini, and Cartesia TTS delivering natural voice conversation.
Start talking to Lucy →Welcome