The promise of Voice AI is immense. AI receptionists and virtual agents have the potential to transform contact centers, offering quicker wait times, lower overhead, and highly responsive service across thousands of daily interactions.
Yet, despite the efficiencies it promises, adoption is slow. Implementing such a large undertaking requires substantial infrastructure and investment. Beyond these challenges, the specter of AI hallucinations remains a significant barrier, especially in regulated industries.
As Jake Tyler, Head of Go-To-Market Strategy at Glia, explains, “Responsible AI is a non-negotiable safety standard for the regulated banks and credit unions we serve.”
These regulated sectors, such as banking and healthcare, which are often called upon in times of urgency, could stand to benefit from the technology the most.
Thus, it’s clear that for Voice AI to really take off, it is essential to assuage these fears. Anything below 100% accuracy is not good enough. To bring in AI Voice without fear, companies need assurances that the AI solution they are adopting is hallucination-free.
Understanding Zero Hallucinations in Voice AI
For enterprises considering Voice AI, the concept of “zero hallucinations” is not just a perk; it’s a baseline requirement. This is especially important when comparing the technology to its text-based AI alternatives.
“As the most commonly used support channel, it has a much higher volume of interactions than other channels — thereby raising the likelihood of hallucinations in general,”
Tyler explains.
Contact centers routinely handle between 10,000 and 50,000 calls per month. Even low-probability errors can scale into hundreds of risky interactions at this volume. A “rare” hallucination becomes a statistical certainty at enterprise scale.
Unlike chat, voice also cannot easily provide links to sources as part of its answer, meaning the response is almost always used standalone without easy ways to verify or validate it.
To eliminate this risk, enterprises are increasingly turning to Voice AI systems that constrain, validate, or pre-approve every response before it ever reaches a caller.
Engineering for Zero Hallucinations: Emerging Guardrails and Best Practices
Industry-wide, several technical patterns are emerging as essential to achieving hallucination-free voice interactions. Surprisingly, few rely solely on “improving the model.”
Instead, systems like Glia Virtual Assistants hinge on controlled generation, human oversight, and validation against known data.
The first method to reduce hallucinations is the careful creation of pre-approved response libraries. Instead of allowing an LLM to freely generate text, enterprises can restrict Voice AI to respond only with content vetted in advance. Tyler describes this as the only dependable approach today:
“Even the best LLMs hallucinate. To be safe for banking, AI needs human validation.”
This approach still allows for a natural, conversational tone but eliminates improvisation entirely. The AI decides which answer to select based on intent detection, but not how to phrase it from scratch — dramatically reducing variability and risk.
Second, knowledge grounding is emerging as a critical best practice. AI voice agents increasingly pull from curated knowledge bases, historical interaction logs, and structured enterprise data rather than relying on model memory alone. This grounding ensures that answers reflect real business rules and documentation.
Third, and perhaps most critical, companies need to implement real-time validation layers — essentially AI safety filters — to cross-check output before it reaches customers. This includes putting a human in the loop. As Tyler says,
“This is the only way to eliminate hallucinations rather than simply reduce them.”
Although AI is meant to reduce the workload associated with this — and this method may seem counterintuitive — time efficiencies can still be gained. Instead of multiple agents manually typing responses, one agent can now oversee them.
These practices collectively form a new architecture of Voice AI, one that favors accuracy and trust over unchecked speed.
Changing the Risk Paradigm: From Fear to Opportunity
Achieving zero hallucinations doesn’t just improve technical performance; it reshapes how enterprises evaluate the risks and rewards of Voice AI altogether. Voice-based agents, previously seen as limited or experimental tools, can now play a greater role in the contact center.
With strict guardrails, pre-approved content libraries, and real-time validation layers, organizations can shift Voice AI from a potential liability to a reliable operational asset.
While accuracy sits at the core, it is not the whole story. Scalable Voice AI also requires low latency, consistent natural language understanding, and smooth handoffs to human agents. Glia, for instance, ensures a strong Voice AI model as the baseline, with high-fidelity transcription, controlled conversational paths, and dynamic escalation logic. This makes the human-in-the-loop monitoring job easier, with less time spent interpreting.
Ultimately, moving beyond fear in Voice AI means embracing architectures that prioritize safety without sacrificing experience. When robust technical foundations, human oversight, and carefully governed response models come together, Voice AI becomes more than an efficiency tool; it becomes a trusted, compliant extension of the organization’s service strategy.
