Carlos Leon@CLeon

VoiceShield is built around a simple question: how can we be sure an AI voice agent is safe before letting real people talk to it? In real life, conversations don’t always go as planned. People can be angry, confused, vulnerable, emotional, or simply unpredictable. Traditional testing usually focuses on expected scenarios, so it can easily miss these difficult situations. VoiceShield tackles this by creating AI-powered personas that act like different types of real callers and put voice agents through realistic conversations. During each test, these personas challenge the voice agent with different situations while VoiceShield analyzes the conversation as it happens. It looks for things that could go wrong—such as unsafe or inappropriate responses, policy violations, risky behavior, or poor handling of vulnerable users. This helps teams discover potential problems before the AI agent is exposed to real customers. We use Speechmatics for voice interaction and speech processing, DataBright to bring relevant EU policies and regulatory information into the evaluation process, and our Smith evaluation agent to analyze conversations and determine risks, violations, and severity. The results are then presented through VoiceShield as clear Pass, Flag, or Fail outcomes with actionable recommendations. Our goal is to make AI voice testing feel less like traditional QA and more like putting an AI agent through a realistic stress test before real customers do it. VoiceShield helps businesses find problems early, understand where their agents are vulnerable, and build voice AI that people can trust.

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