When we first launched our Auto QA solution, we expected companies to adopt it for one reason: to automate the tedious process of manual call reviews. And many did. But over time, something unexpected happened. Our customers started telling us they were getting so much more out of it than just efficiency gains.
They were using Auto QA to understand why customers were really calling. To spot compliance gaps. To detect which agents were quietly struggling. Even to measure the impact of marketing campaigns.We had built a tool for automation, and discovered we had built a lens into the business. And that realization has changed how we think about our own product, our roadmap, and the value we deliver to customers.
Manual QA in contact centers has always been painful. It’s time-consuming, limited in scope, and often subjective. Auto QA was built to solve that: score 100% of calls automatically, surface outliers, and free QA teams to focus on coaching and trends.
And it worked. Companies using Auto QA cut their QA review time by as much as 70%, increased agent coverage dramatically, and saw faster feedback loops. That alone is a big win.
Example of the call review coverage with one of the MiaRec customers - isp.net.
But over time, many of these same companies came back to us with stories—not about efficiency—but about insight.
What started as a productivity tool quickly became something more strategic. Organizations began asking new questions:
Why are our customers calling again and again?
Which of our agents consistently resolve issues faster—and why?
Are we consistently delivering on our compliance obligations?
What impact are our campaigns and offers having on customer sentiment and call volume?
These aren’t QA questions. They’re business questions. And they were being answered not by dashboards or reports—but by conversations.
Take ISP.net, for example. They started with Auto QA to streamline agent evaluation. But what they uncovered changed their operations:
A 15% reduction in repeat calls after addressing confusion in onboarding language
Uncovered marketing misalignment through analysis of campaign-related calls
Doubled QA coverage without increasing headcount
They didn’t just improve QA—they gained operational awareness across departments.
We’ve seen this again and again:
A financial services firm used Auto QA to detect early signals of churn in frustrated client language weeks before cancellations happened.
A retail brand identified that one promotion was generating a spike in confused calls—leading them to quickly adjust the messaging before it spread further.
A national healthcare provider surfaced patterns in non-compliance with key regulatory disclosures—allowing them to train and course-correct before facing potential audit issues.
These aren’t isolated wins. They are examples of the real power of Auto QA when seen not just as a way to automate—but to observe.
This shift mirrors a larger trend: organizations are waking up to the fact that the most valuable business intelligence isn’t in spreadsheets or surveys—it’s in conversations.
Every customer call contains unfiltered, unprompted data: what they care about, what confuses them, what frustrates them, what influences their loyalty. The difference now is that, with AI and Auto QA, we can extract that data—at scale.
It’s not just about tracking CSAT or NPS. It’s about:
Identifying patterns in why people call—and what outcomes result
Connecting agent behavior with CX metrics and business KPIs
Spotting red flags in service delivery before they impact loyalty or revenue
Several unique conditions make CX the ideal proving ground for AI-driven business value:
Data at Scale: CX generates enormous volumes of structured customer interaction data daily.
Clear Link to Revenue: Customer satisfaction and retention metrics are tightly coupled to growth and profitability.
Operational Pressure: Contact centers and CX teams are under constant pressure to deliver more with leaner resources.
In CX, AI isn’t solving abstract problems—it’s answering urgent, bottom-line questions:
Who is at risk of leaving?
Why are they dissatisfied?
How much revenue is at stake if we don't act?
What actions will make the biggest impact?
When AI is applied to these questions, the results become not just visible—but transformational.
For the first time in history, operational leaders have access to a tool that gives them direct, scalable, and unfiltered visibility into the business—without needing a team of analysts or months of lagging reports.
Sure, business leaders have always had access to data. But it was often fragmented, delayed, and difficult to connect across departments. Decision-making has long relied on a combination of gut feeling, static KPIs, and partial visibility.
Auto QA changes that.
Now, CX leaders, compliance heads, revenue owners, and even marketing teams can:
See exactly what customers are saying, at scale
Understand how agents, systems, and messaging are performing in real time
Detect risks and opportunities before they appear in traditional metrics
Make faster, more confident decisions based on complete, contextual insight
This isn’t just a new tool—it’s a new level of control. One that helps leaders stop flying blind and start leading with clarity.
In a world where customers expect more and businesses need to act faster, this kind of insight isn’t just helpful—it’s transformational.
There’s another reason Auto QA is emerging as the first step in a broader business intelligence journey: it’s tangible, ROI-driven, and easy to adopt.
Unlike other conversation intelligence tools that can feel abstract or suffer from "blank canvas syndrome," Auto QA begins with something every contact center already has—a manual scorecard. That’s your foundation. From there, automation simply accelerates what you're already doing and builds credibility for broader AI adoption.
It’s a rare case where the value is immediate and measurable:
You replace a time-consuming manual process
You expand coverage from a small sample to 100% of interactions
You gain insights that go far beyond what human QA teams can detect
And perhaps most importantly: the learning curve is low. You don’t have to reimagine your processes—you just automate what already exists and then build from there.
In fact, we’ve outlined exactly how to translate a manual QA scorecard into an automated one in a recent post [link to blog post]. It's one of the most accessible, high-ROI starting points for organizations looking to modernize CX and unlock the power of their voice data.
Auto QA might be the first step into AI-powered transformation—but it doesn’t have to be the last. Once organizations experience the visibility and intelligence that come from analyzing 100% of their customer conversations, the natural next step is to scale those insights across the business.
From Auto QA, many of our customers expand into Voice of Customer (VoC) intelligence—automatically measuring CSAT, NPS, and Net Effort Score (NES) without surveys. This gives teams continuous feedback across all interactions without adding friction to the customer experience.
From there, it evolves further: combining QA, CX, and operational insights into a true business intelligence platform, where every department can leverage conversation data to improve decision-making.
Auto QA is the gateway. But what it unlocks is an entire ecosystem of AI-driven intelligence—clear, actionable, and transformative.
We thought we were building automation. And we were. But what we delivered—through our customers’ innovation—was something much more meaningful.
Auto QA is becoming one of the most powerful sources of business intelligence.
It helps companies see clearly: what customers are experiencing, what agents are doing, and where systems are breaking down.
Automation got us in the door. But visibility is what’s keeping us in the conversation.
We’re proud of how far the platform has come. And even more excited about where it’s headed next.