If you run quality assurance at a contact center, you already know the math doesn’t work.
Your agents handle thousands of interactions every week. Your QA team manually reviews a fraction of them, sometimes just 1% to 3%. You build your scorecards, calibrate your evaluators and do your best to find meaningful patterns in a small sample that may not reflect what’s actually happening across the contact center.
The result? Compliance issues that slip through. Coaching gaps that compound. Agent performance trends that don’t surface until they’ve already affected your customers or your numbers.
This is the QA problem contact centers have lived with for years. It’s not a people problem. It’s a math problem. And scoring every interaction instead of a small sample changes what’s possible.
Capacity Auto QA is AI-powered automated quality assurance software that scores 100% of voice calls, chats and helpdesk tickets against your own QA scorecards, then connects the results to agent coaching.
What Is automated quality assurance software?
Automated quality assurance (Auto QA) software uses AI to evaluate customer interactions against predefined quality standards, reducing the need for manual reviews.
Instead of relying on QA analysts to listen to individual calls or read conversations, automated QA for contact centers analyzes interactions at scale to identify performance trends, compliance concerns, coaching opportunities and customer experience issues.
Capacity Auto QA applies LLM-based analysis and voice analytics to customizable scorecards. Teams define their evaluation criteria, and the software applies those standards across their interactions.
By gathering insights from every interaction, no matter the channel or the agent type, teams can better understand where agents are struggling, which behaviors contribute to escalations, what’s driving longer handle times and where coaching could have the greatest impact. CX leaders can then optimize how their contact center performs, ultimately improve customer experience and lower operational costs.
Capacity Auto QA also integrates with a Coaching Hub, connecting evaluation results to targeted feedback workflows so insights lead to action.
Manual QA vs. Capacity Auto QA
| Feature | Capacity Auto QA | Manual QA (sampling) |
|---|---|---|
| Coverage | 100% of interactions | Typically 1–3% of interactions |
| Channels | Voice, chat and helpdesk tickets | Often individual call reviews |
| Scoring | AI applies customizable scorecards | Evaluators apply scorecards manually |
| Consistency | Applies defined criteria consistently at scale | Requires evaluator calibration |
| Coaching | Connected Coaching Hub | Often separate manual follow-up |
| Time to value | Can analyze 30 days of historical interaction data after go-live | Manual processes require ongoing review time |
Automated QA doesn’t eliminate the need for human judgment. QA leaders still play an important role in setting standards, reviewing exceptions, validating results and determining how to improve performance.
Why is auto QA better than manual QA?
When Thrasio implemented Capacity Auto QA, its team was manually reviewing approximately 3% of calls. After implementation, it moved to 100% coverage.
Achieving that same level of review manually would have required approximately 528 additional QA analysts.
Instead, Thrasio automated the evaluation process, saving roughly $260,000 annually while achieving 97% CSAT.
Greater coverage also helped reveal patterns that would have been difficult to identify through sampling alone, including a call driver responsible for 62% of volume that added three minutes per interaction.
Consider what can go unnoticed when most interactions aren’t reviewed:
- Compliance issues that occur outside the sampled calls
- Recurring customer questions that increase handle times
- Knowledge gaps affecting multiple agents
- Opportunities to improve workflows or reduce escalations
- Coaching needs that aren’t visible in individual evaluations
Another Capacity customer, BCLC, used Auto QA-driven coaching insights to achieve a reported 200% increase in NPS, a seven-point improvement in CSAT and a 56% reduction in difficult calls.
The opportunity isn’t simply reviewing more interactions. It’s using the additional visibility to make better decisions.
with Auto QA
5 best practices for improving contact center quality assurance
Whether you’re using manual evaluations or automated QA software, these five practices can help make your call center quality assurance program more effective.
1. Focus your scorecards on what matters
Avoid scorecards with too many criteria that don’t directly affect the customer experience.
Prioritize measures such as issue resolution, accuracy, compliance, empathy and process adherence. Review your scorecards regularly to make sure they reflect current business priorities.
2. Look for patterns, not just individual mistakes
One low-scoring interaction doesn’t necessarily indicate a performance problem.
Look for recurring issues across agents, teams and interaction types. For example, if multiple agents struggle with the same customer question, the problem may be outdated knowledge or a process that needs improvement rather than individual performance.
3. Make coaching specific and actionable
QA scores are most useful when agents understand what they can do differently.
Instead of simply sharing a score, identify specific behaviors to improve, provide examples and follow up to see whether coaching made a difference.
4. Calibrate evaluations regularly
Even experienced QA teams can interpret scoring criteria differently.
Regular calibration sessions help evaluators apply standards consistently. If you’re using automated QA, periodically review AI-generated scores against human evaluations to identify discrepancies and refine your criteria.
5. Turn QA findings into operational improvements
Don’t limit QA insights to agent coaching.
Use recurring issues to identify gaps in knowledge, workflows, policies and customer communications. Track whether the changes you make actually improve resolution rates, customer satisfaction or other important outcomes.
The goal is to move beyond measuring quality to continuously improving it.
QA should be a continuous improvement process
Traditionally, quality assurance has focused on evaluating past interactions and helping agents improve their performance.
But the insights gathered through QA can have a much broader impact.
For example, repeated questions about a policy may reveal that customers can’t easily find the information they need. A pattern of incorrect answers may indicate outdated knowledge. Frequent escalations may point to a workflow that needs to change.
Those findings can inform improvements to knowledge bases, agent guidance, automated workflows and AI agents.
Within Capacity’s platform, post-interaction insights can feed into AI agent configuration and real-time agent assist, helping organizations improve how both human and AI agents handle future conversations.
This creates a continuous improvement cycle:
- Evaluate customer interactions.
- Identify recurring issues and opportunities.
- Improve coaching, knowledge or workflows.
- Apply those improvements across customer support channels.
- Measure the results and continue refining.
Instead of treating QA as a reporting function, organizations can use it to continuously improve customer experiences.
What to look for in auto QA software?
If you’re comparing automated quality assurance tools for your contact center, consider more than the percentage of interactions each platform can score.
- Coverage and channels: Does the software evaluate every interaction or just a larger sample? Can it analyze voice calls, web chats and SMS messages? Does it score AI agents as well as human agents?
- Customizable scorecards: Can you use your own QA criteria and adjust them as business priorities change?
- Scoring accuracy and calibration: Can your team validate AI-generated evaluations, review exceptions and refine scoring criteria?
- Coaching capabilities: Can managers easily turn evaluation findings into specific coaching opportunities, or do insights remain in a dashboard?
- Implementation and integrations: Does the software connect with your existing contact center systems? Can it analyze historical interactions to help you get started?
- Compliance and reporting: Can you identify potential compliance issues and provide supporting interaction evidence?
- Continuous improvement: Can insights help improve knowledge, workflows, AI agents and real-time agent guidance?
The right solution should help your team spend less time conducting routine evaluations and more time acting on what those evaluations reveal.
Capacity Auto QA combines automated scoring, customizable criteria, coaching workflows and post-interaction intelligence within a unified CX automation platform.
Which teams benefit most from auto QA software?
Automated QA can benefit several teams within a contact center, including:
- QA managers gain broader visibility into interaction quality without needing to expand manual review teams at the same rate as interaction volume.
- Contact center operations leaders can identify recurring performance issues, process gaps and opportunities to improve efficiency.
- Coaching and training teams can use evaluation findings to prioritize coaching and measure whether performance improves.
- Compliance teams can identify potential risks across a larger share of interactions and investigate flagged issues.
The common benefit is having more complete information to guide decisions rather than relying on a limited sample.
Turn more customer interactions into better experiences
Reviewing only a small percentage of customer interactions makes it difficult to see the full picture.
Automated QA gives contact centers a way to evaluate interactions at scale, identify recurring issues and focus their teams on improvements that matter.
Capacity Auto QA brings those capabilities together with coaching, conversation intelligence and other post-interaction workflows, helping organizations turn quality insights into better performance across human and AI agents.
Want to see what 100% QA coverage could look like for your contact center? Book a demo with Capacity.
100% of interactions with Auto QA.
FAQs
Auto QA, or automated quality assurance, uses software to evaluate customer interactions against predefined quality criteria. Instead of relying entirely on manual reviews, AI can analyze calls, chats and other interactions at scale to identify quality trends, compliance concerns and coaching opportunities.
Manual QA relies on people reviewing individual interactions, usually a small sample of total volume. Automated QA uses AI to evaluate interactions at scale, allowing teams to identify patterns that might otherwise be missed. Human oversight remains important for calibration, exceptions and coaching decisions.
Traditional manual QA programs often evaluate only a small percentage of calls because of time and staffing constraints. Automated QA makes it possible to score 100% of eligible interactions, giving teams broader visibility into customer experience and agent performance.
Many automated QA solutions support customizable scorecards. Capacity Auto QA allows organizations to define their own evaluation criteria and apply them consistently across interactions.
Not necessarily. Automated QA reduces the time spent on routine evaluations, allowing QA professionals to focus more on calibration, investigating trends, coaching and improving processes.
Capacity Auto QA supports voice calls, chats and helpdesk tickets.
Implementation timelines vary by platform and integration requirements. Capacity Auto QA offers pre-built integrations and more than 1,200 out-of-the-box insights, with the ability to analyze 30 days of historical interaction data shortly after go-live.
