- We were working on voice agents in healthcare, and kept running - into the same problem: manually testing was incredibly - time-consuming and error-prone. Testing voice AI in a - comprehensive way was far more difficult than we had anticipated - – not just the setup, but the ongoing monitoring of production - calls. Despite our best efforts, some calls still failed once we - went live. -
-- The main challenges we faced were: (1) Demonstrating reliability - to customers for production was really tough; (2) Manual testing - was incomplete and didn't cover edge cases; (3) We couldn’t - easily simulate all possible conversations, especially with - diverse customer personas; (4) Monitoring every production call - manually was a huge time sink. -
-- We built Vocera to solve these problems. Vocera automatically - simulates real personas, generates a wide range of testing - scenarios from your prompts/call scripts and monitors all - production calls. The result? You can be sure your voice agents - are reliable, and you get real-time insights into how they’re - performing. -
-- Our platform tests how your AI responds to diverse personas, - evaluates the conversation against different metrics and gives - you directed feedback on the issues. -
-- What’s different about us is that we don’t just automate the - evaluation. We generate scenarios and metrics automatically, so - developers do not have to spend time defining their scenarios or - eval metrics. This saves them a ton of time. Obviously, we give - them the option to define these manually as well. Also, we - provide detailed analytics on the agent's performance across - simulations so developers do not need to listen to all call - recordings manually. -
-- If you’re building voice agents and want to ensure they’re - reliable and production-ready, or if you’re just interested in - the challenges of Voice AI, we’d love to chat. -
-- We’d love to get your feedback, thoughts, or experiences related - to testing voice agents! -
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