How a 300ms Voice AI Setting Was Cutting Off 22% of Users Mid-Sentence

A developer discovered that setting Deepgram's endpointing parameter to 300 milliseconds caused their voice AI agent to interrupt users mid-thought in 22% of long responses, after analyzing 1,140 user turns from one week of logs. The endpointing value determines how many milliseconds of silence Deepgram treats as the end of a speaker's turn, but natural thinking pauses in conversation often last 900ms or more. Raising the threshold to 1,200ms reduced cutoffs to 6% but added noticeable latency to every interaction. The developer instead implemented a two-stage fix: keeping the 300ms trigger as a tentative signal, then running a lightweight completeness check using a trailing-word heuristic and a small LLM classifier before the bot responds. This approach brought the cutoff rate down to 3.1% while adding only about 140 milliseconds of median delay, though it required discarding 19% of speculatively generated replies.