Plivo

For teams moving beyond a proof of concept, start with 5 Voice Agent Failure Modes You'll Hit in Week One. Venky discusses the tradeoffs among response speed, cost, instruction following, and tool use. He describes a target below 550 ms from the caller finishing speech to first audio, and favors smaller self-hosted open models for consistently low model-layer latency. Treat these as the speaker’s recorded guidance, rather than universal benchmarks.

Return to the same talk for transcription failures involving names, jargon, numbers, addresses, accents, and mixed languages. Venky connects dynamic keyword boosting and input normalization with typed data collection: validators, confirmation rules, and tool-assisted resolution for individual fields. The roughly 30% to 95% collection-accuracy improvement is a result Plivo reports in the recording, not an independently verified or current performance guarantee. This path is useful for planning field-level tests.

The recording also provides a checklist for preparing model output before text-to-speech: remove markup and emoji, expand dates and currencies, handle email addresses, apply pronunciation dictionaries, and slow down sensitive entities. It briefly introduces end-of-turn detection, interruptions, and backchanneling. Use that closing discussion as an introduction to those concerns; the supplied transcript summary leaves pipeline terminology unresolved, so it does not support a claim about which pipeline implements them.