Manual QA Doesn't Scale for Voice AI. Start There Anyway.
Blog post from Coval
Manual QA is an essential starting point for voice AI testing, where human testers manually assess call quality by listening to recordings and reading transcripts. This approach is effective at low call volumes, providing valuable insights into failure modes, background noise, accents, and other nuances that automated systems may miss. However, as a voice AI system's capabilities and user base grow, teams often face challenges such as increased call volumes, faster shipping cycles, production issues, expanded functionality, and language coverage gaps, all of which signal the need to transition to automated evaluations. Engineers typically recognize these limitations before leadership, as they experience firsthand the inefficiencies and opportunity costs associated with extensive manual testing. To advocate for automation, it's crucial to frame the discussion in terms of each stakeholder's priorities, highlighting direct costs, opportunity costs, and risks to product quality and compliance. A balanced approach, integrating both manual and automated QA, ensures comprehensive coverage and accommodates the evolving complexity of voice AI systems.
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