December 17, 2025 • 4 min read
Retell AI Introduces Automated QA for Enterprise Voice AI
CEO & Founder
December 17, 2025
Quality assurance has been one of the biggest friction points holding Voice AI back from true enterprise scale. Retell AI’s new automated QA capability aims to eliminate that bottleneck entirely.
Voice AI has crossed the experimentation phase and Enterprises are no longer asking if AI can handle customer phone conversations. They’re asking whether it can be trusted at scale, across millions of interactions, without constant human oversight.
That question has become the biggest barrier to enterprise adoption of Voice AI.
With the launch of Retell Assure, its new automated quality assurance solution, Retell AI is directly targeting that gap, introducing what may be the most important infrastructure layer in the Voice AI stack today.
From Pilot Success to Enterprise Reality
Over the past two years, Voice AI platforms have made enormous strides in speech recognition, latency, and conversational realism. Many organizations have proven that AI agents can handle tier-one calls effectively.
But moving from pilot to enterprise rollout has exposed a structural problem.
Most Voice AI systems are still governed using manual QA models designed for human agents:
Small samples of recorded calls are reviewed by QA teams
Issues are flagged and routed back to AI engineers
Model updates take days or weeks to deploy
At enterprise scale, this approach collapses under its own weight.
When AI agents are handling millions of calls per month, reviewing 1–2 percent of interactions isn’t quality assurance—it’s blind trust.
Retell Assure reframes quality assurance entirely
Retell Assure is built on a simple but disruptive premise: Every AI call should be monitored, not sampled.
The platform continuously monitors 100% of Voice AI interactions, automatically identifying performance issues and adjusting models in near real time without human intervention.
This eliminates one of the most fragile links in Voice AI operations: the delay between customer impact and corrective action.
"Automated quality assurance was the #1 request of our enterprise customers - our fastest-growing customer segment. These companies are deploying voice AI at high scale and need to be sure the technology remains solid over time - Retell Assure lets them do that without requiring human agents to monitor calls. This is a big step forward in our strategy to become the AI Voice agent platform of choice in the enterprise.
These organizations aren’t just concerned with whether Voice AI works today. They need confidence that it will keep working tomorrow, at scale, without degrading customer experience.
Why Automated QA is the Control Plane for Voice AI
The deeper story here isn’t QA, it’s control.
As Voice AI takes on more responsibility at the front line of customer experience, enterprises need:
Continuous verification that AI behavior aligns with business intent
Guardrails that prevent silent performance drift
Visibility into how AI decisions affect real customers in real time
Automated QA effectively becomes the control plane for Voice AI systems—governing performance, compliance, and CX outcomes continuously rather than retrospectively.
This shift mirrors what happened in cloud infrastructure and DevOps: once systems reached sufficient scale, automation replaced manual oversight as the only viable path forward.
Why This Matters Now
Retell AI's platform is already operating at meaningful enterprise scale:
40M+ real-time AI phone calls every month
300%+ quarter-over-quarter user growth
3× growth in monthly recurring revenue over the past six months
$35M+ in ARR
At this level of volume, manual QA becomes not just inefficient, but structurally incompatible with the business model.
Automated QA is no longer a “nice to have.” It’s table stakes for platforms that want to power mission-critical customer interactions.
Voice AI is Moving from Tools to Autonomous Systems
The launch of Retell Assure reflects a broader shift.
Voice AI is evolving from a collection of conversational tools into autonomous, self-managing systems.
In this next phase:
Sampling-based QA will feel outdated
Enterprises will expect full interaction coverage by default
Platforms without automated QA will struggle in regulated, high-risk, or high-volume environments
The future of Voice AI won’t be defined by how human it sounds. It will be defined by how reliable, governable, and self-correcting it is at scale.
Automated QA is how the industry gets there.