Shipping Voice AI with quality gates and durable storage
Patterns for voice capture, streaming transcripts, and persisting conversational analytics without sacrificing privacy or reliability.
By Shashank Bhardwaj
Voice products add constraints audio pipelines do not share with text chat: codecs, packet loss, partial transcripts, and sensitive biometric-adjacent data. A robust design separates capture, transcription, downstream reasoning, and persistence so each stage can be retried, audited, and scaled independently.
Quality gates before the LLM
Run lightweight checks on audio and transcript quality—signal-to-noise heuristics, language detection, and profanity or PII filters—before expensive model calls. Pair that with explicit user consent flows and retention policies that are easy to explain in the UI.
Analytics without losing trust
When conversational data lands in systems like Spanner, partition by tenant, encrypt at rest, and minimize fields stored for analytics. Event schemas should be versioned so downstream dashboards do not break when the assistant adds new tools or intents.
Observability for voice is end-to-end: time-to-first-token, word error rate sampling, and drop-off after mic permission. Those metrics tell you whether the problem is UX, network, or model—and they keep launches honest.