Why does my AI need 2 seconds to answer when the speech-to-text is already done?🤔

I'm building a real-time voice AI application, and I ran into a problem I didn't expect.

The speech-to-text part is already streaming and feels fast⚡. But then...

The user stops speaking → transcript is ready → ...nothing happens for a moment → AI finally starts answering.

That gap is surprisingly noticeable.

My current pipeline is roughly:

Microphone → Deepgram Streaming STT → detect question → LLM → streaming response

And now I'm trying to understand where those extra milliseconds are actually going⏱️. There are potentially several places:

* Deepgram endpointing

* Waiting for the final transcript

* Question/utterance detection

* Network round trip

* LLM time-to-first-token

* Prompt size

* Model selection

* Streaming configuration

* How the different stages are orchestrated

The goal is to make the experience feel almost instantaneous; ideally <1 second from the end of speech to the first useful AI output.

So I'm curious:

1) If you've built a real-time voice AI system, what was your biggest latency bottleneck?

2) And what actually made the biggest difference?

I'm especially interested in real-world numbers:

What end-to-end latency have you managed to achieve?👇