7 Best Customer Support Quality Assurance Tools for 2025

Key Takeaways:

• Automated QA tools provide full coverage by delivering more accurate, scalable insights into agent performance and customer sentiment.
• The best QA tools combine customizable scorecards, objective scoring, real-time guidance, and voice of the customer (VoC) insights for continuous improvement.
• We review seven platforms for their distinct strengths, like CRM integration, real-time dashboards, or flexible frameworks that are suited to different team needs and tech stacks.
• Our platform, Level AI, offers deep contextual understanding, emotion detection, and real-time coaching capabilities powered by advanced AI.

Even the most committed QA teams eventually struggle when it comes to maintaining coverage and consistency across thousands of daily customer interactions because of too little coverage, too much effort, or not enough insight.

These gaps in visibility and scalability can hold you back from delivering timely feedback, spotting emerging issues, or driving meaningful improvements to the customer experience.

That’s where automated QA tools come in. They analyze every customer interaction, surface actionable insights in real time, and give your team the clarity and scale that manual QA simply can’t achieve.

To help you find the right tool for your organization, we explain critical features to look for, followed by a list of the leading software options. This list starts with our own QA tool for customer support, Level AI, which analyzes and scores 100% of customer interactions. Level AI is a unified AI platform for the entire customer experience and is used by marquee companies in a variety of different verticals such as ecommerce & retail, financial services, healthcare, printing & gifting, transportation, and tech.

It’s also a recognized leader in QA software and a Pioneer in the CMP Research Prism for Customer Analytics for its delivery of exceptional customer experiences through state-of-the-art AI technology.

In this article, we explore seven tools for customer support QA:

1. Level AI: Best for Analyzing Every Interaction with Deep Context and Intent
2. Observe.AI: Best for QA with Real-Time Scoring and Policy Checks
3. MaestroQA: Best for Teams That Want Flexible QA Frameworks and Reporting
4. Playvox: Best for Teams Using Zendesk, Freshdesk, or Salesforce
5. Talkdesk: Best for QA with Synchronized Voice and Screen Recording
6. Zendesk: Best for Affordable QA Tied to Ticket Performance Metrics
7. NICE CXone: Best for Teams Looking to Combine QA with CRM Data
   
   

Three Features to Look for in a Customer Support Quality Assurance Tool

An advanced platform helps overcome organizational and technological challenges in scaling the QA process, such as not being able to provide agents with the timely feedback they need to improve CX. It combines automation, accurate insights, and strong coaching features, and integrates readily into your existing systems.

Below are four key features that an advanced system should have:

1. Strong Analytics and Full Coverage

An advanced QA tool should analyze all conversations across every channel, including calls, chats, and emails in order to turn that data into actionable insights to improve both customer experience and agent performance.

Such a system uses real AI to uncover the deeper meaning of language rather than simple natural language processing techniques that only identify keywords. Rule-based algorithms struggle to capture the complexity and variety of human speech, often missing the context of a conversation and the intent a customer is expressing.

Even some systems touting themselves as AI really use rule-based algorithms and require you to manually define dozens or hundreds of word combinations or partial matches to capture what a customer might mean when they use certain words.

Unfortunately, if a customer expresses their intent using phrasing that wasn’t explicitly foreseen and therefore wasn’t entered into the system, the intent might be missed.

For example, a keyword system programmed to look for "refund" or a handful of other possibilities might miss variations like "Can I get my money back?" or “I’d like to return this item and get credited.", resulting in an incomplete or inaccurate understanding of what they mean.

They can also misclassify interactions if the same words are used with opposite intentions or sarcasm, like “I love waiting on hold.”

Rule-based algorithms also tend to provide simplistic and often inaccurate views of customer feelings, such as relying on binary classifications (positive or negative) to interpret sentiment, which fails to capture the full spectrum of nuanced emotional states like worry, anger, disappointment, or excitement.

When it comes to voice analysis, some conventional analytics tools try to measure emotions based on pitch and tone, but these methods are prone to error and may misinterpret shifts in vocal characteristics (e.g., excitement or nervousness) as agitation.

On the other hand, systems relying on true AI use sentiment analysis tools that are much more effective at identifying sentiment and intent, as well as understanding the context of conversations. They analyze entire sentences and conversational patterns using natural language understanding, which allows them to grasp meaning, tone, and intent even when phrasing or wording differs from what’s expected.

2. Customization and Consistent Scoring

Buyers should look for features that let them tailor their QA process to specific business objectives while ensuring that agent performance is measured fairly and accurately across 100% interactions. Examples include:

• QA scorecards that offer extensive customization capabilities that give organizations the ability, for instance, to add or remove different QA categories, or separate scorecards for different channels or teams.
• Dashboards and reporting where admins, managers, and agents can view data about team performance and filter reports by agent, ticket type, or date range.
• Personalized reports for agents, which show their recent scores, evaluator comments, and progress over time.

Call center analytics software that consistently and automatically scores agent performance, customer sentiment, and other aspects of interactions also lead to fair and objective assessments.

3. Instant Insights for Immediate Action

Effective real-time QA relies on continuous analysis of all customer interactions currently in progress, which in turn allows supervisors and agents to address inconsistent approaches immediately and prevent these from impacting the customer experience or becoming habitual errors.

Live QA systems can also provide agents with instant, contextual guidance during conversations, boosting productivity and improving service quality. For example, they can show recommended knowledge articles and resources retrieved from connected internal sources, such as knowledge bases and ticketing systems, a feature generally known as real-time agent assist.

This reduces the need for agents to manually search for answers, decreasing multitasking and cognitive stress while improving focus on the ongoing conversation.

Top Quality Assurance Tools for Customer Support

1. Level AI: Best for Analyzing Every Interaction with Deep Context and Intent

Level AI uses generative AI, natural language understanding, and semantic intelligence to deeply analyze 100% of customer interactions, allowing organizations to capture the full context, intent, and sentiment behind customer interactions. This leads to accurate quality assessments and stronger coaching programs.

It automates the most time-consuming aspects of QA so teams can focus on high-impact coaching and continuous improvement, rather than on manual scoring.

Our call center quality assurance software also gives evaluators and agents a clear view of what customers are saying by surfacing VoC insights and uncovering recurring themes, sentiments, and emerging issues from customer conversations.

The platform’s real-time tools for agents and managers make it easier to resolve issues as they happen, improving resolution speed and the customer experience in the moment.

Below, we discuss the QA features of Level AI that allow it to replace manual processes with automated, context-aware evaluations that pinpoint meaningful trends and performance gaps.

Automatically Capturing Real Intent and Emotions in Interactions

The tiny sample sizes typical of manual QA processes makes it challenging to consistently identify coaching opportunities, compliance issues, or broader customer complaint patterns across conversations.

Level AI solves this limitation by using AI call analytics to uncover what customers really mean when they express frustration, ask questions, or share feedback, revealing the true intent and sentiment behind every conversation.

Our Scenario Engine uses advanced natural language understanding to group similar interactions by intent and context to reveal recurring themes, find root causes of customer issues, and identify the highest-impact coaching opportunities across every channel.

The Scenario Engine understands intent regardless of the specific phrasing used, and classifies every recognized intent as a scenario.

For instance, it recognizes when a customer says "I need to send this rug back" or "I want to return this rug," and classifies these under the “Product Return” scenario. Our call center performance management software comes with a number of scenarios out-of-the-box but you can also define your own according to your business needs.

When the Scenario Engine detects a specific intent, it applies a conversation tag to the relevant phrase or moment within the interaction transcript. These tags are searchable, allowing you to surface conversations containing such tags.

This allows QA teams to quickly pull up curated, filtered lists of conversations where certain intents were expressed, such as customers having login issues or an agent needing to follow up.

Level AI’s near-human understanding of language also allows it to detect and label eight distinct emotions experienced by customers (the highest number of any software in its category), providing a granular view of their feelings:

• Anger
• Annoyance
• Disapproval
• Disappointment
• Worry
• Happiness
• Admiration
• Gratitude

When an emotion is detected in conversation, Level AI applies a sentiment tag to the specific phrase or moment. Tags are searchable and filterable, enabling QA teams to easily pull up curated lists of interactions where customers expressed, for example, disappointment or gratitude.

This allows teams to track how agents handle moments of specific negative sentiment and identify areas for coaching. Level AI also scores the customer experience using a weighted Sentiment Score, which ranges from 0 (strongly negative) to 10 (strongly positive).

This calculation places a greater emphasis (higher weighting) on sentiments expressed later in the conversation, particularly after a potential resolution:

This weighting is based on the idea that a customer’s sentiment at the end of an interaction, after their issue is resolved or escalated, is a more accurate reflection of their lasting impression of both the agent and the brand than their initial mood.

For example, if a customer begins the conversation feeling frustrated but leaves satisfied due to the agent’s handling of the issue, a high final Sentiment Score accurately captures the successful resolution and quality of service. It also indicates a higher likelihood that the customer will retain a positive perception of the brand overall.

Objective Scoring and Feedback for Targeted Coaching Scale

Level AI’s call quality monitoring tools uses an automated scoring and review system to overcome the challenge of subjectivity and inconsistency when using traditional QA methods to sample customer interactions.

Scoring That Aligns with Your QA Standards

InstaScore automatically scores agent performance to eliminate subjective bias and deliver consistent, objective performance assessments for every agent interaction.

It’s displayed as a single percentage value next to every conversation in our QA dashboards, providing an instant snapshot of how well the agent met your defined quality standards and rubrics such as:

• Did they verify the customer’s identity?
• Did the agent use clear, jargon-free language?
• Were all required disclaimers or legal scripts read?
• Did the agent thank the customer and offer further help?

By applying the exact same rules and benchmarks to every interaction, InstaScore ensures performance evaluations are consistent and fair, regardless of human variability or bias.

It also frees up QA staff time so they can concentrate on high-value, qualitative tasks, like coaching. Clicking on any InstaScore value on our dashboards displays specific rubric items the agent met or missed, along with other evidence behind the particular value, like the interaction transcript with notable timestamps.

One of our customers was able to accurately auto-score agent performance, which resulted in a reduction in agent attrition by 30%.

Quickly Identify Interactions That Need Attention

To find coachable or problematic moments during conversations without having to listen through the entire recording, our InstaReview feature flags these based on criteria like low satisfaction scores or high assist counts.

It automatically surfaces specific positive or negative characteristics so you can better focus your time on those interactions that need attention, rather than reading or listening through interactions one by one.

For instance, InstaReview focuses on conversations with:

• Low customer satisfaction, like a low Sentiment Score or CSAT score.
• A higher-than-average duration or a high number of agent-requested assists.
• Negative conversational moments or interactions triggering compliance flags.

Such detected characteristics are indicated by multi-colored tags in our conversation dashboards:

Once InstaReview tags a conversation, managers can view further stats about the conversation in question and listen through it to understand the root cause of the issue and then initiate a coaching session directly from the dashboard.

This provides call center coaching sessions with the context needed when addressing specific, identified performance gaps.

Customer Insights That Don’t Rely on Surveys

Traditional methods of gathering VoC data, which rely on post-interaction surveys (like for CSAT, NPS, and CES), have drawbacks such as including low response rates and inherent human biases. These tend to overrepresent the extreme views of either highly satisfied or highly dissatisfied customers, neglecting the valuable feedback from the "middle majority."

Level AI bypasses these limitations by deriving VoC data directly from an analysis of every interaction. Our VoC Insights proactively surface subtle, emerging trends and pain points that teams might not have been aware of, or that customers may not explicitly mention in a survey.

Such insights allow organizations to identify and address the root causes of customer dissatisfaction proactively. Examples might include:

• Recurring confusion among customers about a new pricing model or plan structure.
• Moments where agents consistently give unclear or inconsistent answers.
• Customers frequently mentioning competitor names, revealing comparison shopping behavior.
• Unexpected product usage patterns based on how customers describe their needs.

This data is aggregated and displayed in intuitive dashboards that allow users to view current trends, filter by complaint categories, and track customer concerns over time.

Level AI also provides flexible and customizable analytics to help teams understand the "why" behind performance scores and CX trends by consolidating data from across the enterprise.

Unifying QA and CX Data Across Systems

Our Query Builder integrates data from disparate internal and external sources (such as CRMs, knowledge bases, ticketing systems, and third-party survey tools) with Level AI’s own conversational data (like conversation tags, sentiment tags, InstaScore, CSAT, etc.), allowing for a holistic view of the customer journey:

This unified approach lets you create tailored reports and dashboards to answer complex related to CX, such as:

• Which customer issues are being resolved fastest and why?
• What call topics are most frequently associated with low satisfaction scores?
• What impact do specific training programs have on agent performance metrics?
• How does agent response time impact overall customer sentiment?

Such custom analytics are displayed in dashboards that are configurable, shareable via email, and that allow for role-based access control, ensuring that every manager has access to the most relevant data for their team or group.

Real-Time Capabilities for Faster Decision-Making

Level AI's call center real-time reporting supports agents and lets managers proactively intervene during live interactions. This reduces friction points like hold times and service errors, leading directly to faster resolution and higher customer satisfaction.

Actionable KPIs While Conversations Are Still Happening

Real-Time Manager Assist equips supervisors with the situational awareness needed during calls, displaying KPIs like customer sentiment, agent performance, and customer conversion probability so they can step in when it matters most.

It offers them a high-level overview of all active conversations via its centralized dashboard, which displays critical metrics updated in real time, including:

• Live Sentiment Score to track the customer’s emotional reactions, showing rises or drops in sentiment along with context (it displays snippets of dialog when you click on the Scores). This gives managers a clearer picture than simple KPIs like call duration and acts as an early warning of potential churn.
  
  
• The agent's performance via real-time InstaScore, allowing managers to quickly correlate performance with customer sentiment.
  
  
• Coachable Insights, which appropriately displays characteristics like "high call time," and "agent recommended a good solution,” highlighting areas where an agent may be struggling or excelling.

The dashboard gives managers immediate insight into what’s happening in the call so if they choose to escalate, they’re already entering the conversation with some important context, minimizing the need for agents to explain the situation.

Clicking any of the KPIs shows supporting data:

Real‑Time Guidance Directly in the Flow of Support

Real-Time Agent Assist minimizes agent stress and customer friction by using semantic intelligence and NLU to understand the conversation's topic and intent as it’s spoken, and proactively deliver contextual help during the course of the interaction.

It continuously analyzes the interaction as it happens and dynamically updates a feed on the agent’s screen with information relevant to the topic being discussed, pulling from integrated sources like the organization's knowledge base, CRM, and ticketing systems

This feed contains:

• Actionable hints, warnings, and FAQs.
• Recommended knowledge articles and resources.
• Relevant scripts and guidance.
• A live transcript of the conversation.

By surfacing the information at the moment of need, Agent Assist greatly reduces the need for agents to search for information or ask colleagues for help, or put customers on hold.

Customers using Agent Assist have reported significant reductions in average handle time (AHT), such as a 13% decrease in one case, and reduced call hold times during peak hours.

Build a More Accurate, Scalable, and Customer-Centric QA Program

Traditional QA tools only scratch the surface, while Level AI delivers the depth and scale needed to fully understand what your customers are saying. Our platform also connects with other core platforms, like CRMs, support tools, and collaboration apps, to unify insights and streamline your QA workflows.

To get started, book a free demo to learn about how Level AI’s customer support QA can drive better business outcomes.

2. Observe.AI: Best for QA with Real-Time Scoring and Policy Checks

Observe.AI automates customer support interactions across voice and chat channels. Its core focus is delivering natural conversations, monitoring agent performance, and ensuring compliance. It positions itself as a solution for enterprises wanting to improve support quality, agent performance, and customer experience.

The key features include:

• Automation of end-to-end call and chat interactions, and driving consistent quality and resolution across support channels.
• Entity extraction and caller intent detection during multi-turn conversations, supporting robust QA processes for diverse customer inquiries.
• Evaluation checks with LLM-driven scoring to ensure accuracy, tone, policy adherence, and safety throughout customer support workflows.
• Integration with existing support tools to deploy quality programs on top of established support operations.

Observe.AI doesn’t disclose pricing on its website.

3. MaestroQA: Best for Teams That Want Flexible QA Frameworks and Reporting

MaestroQA is a customer support quality assurance platform that helps service teams systematically evaluate, coach, and improve agent performance across voice, chat, email, and ticket-based interactions. It allows managers to build customized QA programs, automate grading, and drive continuous improvements to customer experience metrics like CSAT, FCR, and AHT.

MaestroQA:

• Automatically evaluates support tickets and conversations using AI-driven scoring to scale QA coverage and reduce manual workload.
• Allows organizations to design scorecards aligned with their brand values and support goals, ensuring consistent and transparent performance measurement.
• Integrates quality data into coaching sessions to help agents learn from evaluations and improve performance over time.
• Provides data visualization tools, KPIs, and reports that unify QA insights with CRM data and ticketing metrics for trend analysis.

You must sign up for a demo for pricing information.

4. Playvox: Best for Teams Using Zendesk, Freshdesk, or Salesforce

Playvox by Nice automates QA in contact centers and customer support teams by integrating with CRM and helpdesk tools like Zendesk, Freshdesk, and Salesforce. It allows QA managers to review and score interactions, provide real-time feedback, and connect QA results with coaching and performance management to drive continuous improvement.

Core features of Playvox include:

• AI-powered evaluations and scorecards to automatically score interactions, reduce manual effort, improve consistency, and ensure fair, unbiased assessments.
• The capability to connect QA evaluations directly with coaching sessions and Playvox’s lightweight learning management system for targeted learning and quizzes to agents.
• Performance tracking of agent KPIs like CSAT, NPS, and AHT, and generating detailed analytics dashboards for continuous monitoring of quality trends.
• Surfacing of coaching opportunities and sentiment insights from customer interactions, helping QA teams pinpoint behavior patterns and training needs quickly.

Playvox’s least expensive pricing is around $70 per user per month, which includes routing for over 30 support channels and a variety of prebuilt data visualization dashboards and reports.

5. Talkdesk: Best for QA with Synchronized Voice and Screen Recording

Talkdesk’s Quality Management module lets teams evaluate, coach, and optimize agent performance, while ensuring compliance and improving CX. It uses generative AI and analytics to evaluate every customer interaction (voice, screen, and chat) for performance, sentiment, and procedural accuracy.

Talkdesk allows you to:

• Evaluate all interactions using AI to analyze tone, intent, sentiment, and keyword patterns to reduce manual QA effort and ensure consistent scoring across channels.
• Capture synchronized audio and agent screen activity to give QA teams full context of each customer support interaction.
• Identify topics, emotional tone, and compliance risks to highlight coaching opportunities.
• Build flexible quality scorecards aligned with company standards, or choose from prebuilt templates to standardize evaluations.

Talkdesk pricing starts at $85 per user per month for the CX Cloud Digital Essentials plan.

6. Zendesk: Best for Affordable QA Tied to Ticket Performance Metrics

Zendesk allows organizations to ensure consistent, high-quality support through centralized ticket management, automation, collaboration, and detailed performance analytics. This lets support teams maintain compliance with service standards, improve agent productivity, and improve customer satisfaction across multiple support channels.

Zendesk’s features for customer support QA include:

• Dashboards and analytics to track agent performance, customer satisfaction, ticket trends, and SLA compliance.
• Conversation analysis that highlights gaps in customer service and identifies coaching opportunities for agents.
• Survey design assistance that helps create surveys for collecting VoC data.
• Customizable scorecards allowing you to define evaluation criteria, weight key behaviors, and measure agent performance, tailored to the needs of your business.

Zendesk QA’s pricing starts at around $20 per agent per month.

7. NICE CXone: Best for Teams Looking to Combine QA with CRM Data

NICE CXone is a customer experience platform used for customer support quality assurance. It helps organizations systematically evaluate customer interactions across voice and digital channels, enabling consistent, data-driven performance improvements and compliance monitoring. It also integrates automatic conversation scoring, sentiment analysis, and embedded generative AI to speed and refine the evaluation process.

Key features of NICE include:

• Automatic scoring across all channels for comprehensive insights into sentiment and behavior.
• Real-time dashboards and interactive quality reports for team and agent performance monitoring.
• Agent coaching tools with targeted, actionable feedback to improve skills and compliance.
• Integration with CRM systems to provide detailed insights into customer engagement and context.

Pricing starts around $70 per user per month, depending on the modules you want to use. You can also get the entire suite of CXone products for around $250 per month.