Top 9 AI quality management software for contact centers in 2026 (Updated in Oct)

Introduction

Contact center leaders are under more pressure than ever to prove that AI delivers results. A Gartner survey of 321 customer service and support leaders found that 91% reported pressure from executive leadership to implement AI, and leaders named improving customer satisfaction, operational efficiency, and self-service success as their top priorities for 2026. At the same time, 85% of service leaders are adding new tasks and responsibilities to frontline agent roles, which makes consistent quality measurement and coaching harder, not easier. gartnergartner

Quality assurance is where that pressure shows up first. Most customer service departments employ one QA staff member for every 50 to 100 reps, which means only about 1% to 2% of interactions ever get reviewed. That makes it hard to give agents meaningful feedback, spot compliance risks, or identify patterns in customer complaints when the vast majority of conversations go unscored.

Automated quality management (AQM) software solves this by using AI to analyze and score 100% of customer interactions. QA teams can see performance at the individual agent level and across the department, while spending their time on coaching instead of listening to calls.

In this article, we explain what AQM software is, how it works, and what to look for. We then compare nine automated quality management solutions, starting with our own product, Level AI.

What is automated quality management software, and how does it work?

In contact centers, AQM software automates certain quantitative aspects of quality management that evaluators traditionally had to do themselves, such as having to listen through entire conversations and score them according to rubrics.

While automated quality assurance software varies in sophistication, more advanced versions use machine learning and generative AI to evaluate all customer interactions and determine their intent.

This ability to understand human conversation, ascertain intent, and summarize these findings takes a huge burden off evaluators’ hands so they can focus on higher-level, qualitative tasks, such as coaching and advising adjustments to agents’ scripts to improve the customer experience.

But this deep comprehension of words and their context in conversation means more than recognizing meanings and intent; it opens up new ways of automating quality management workflows.

For example, these systems can:

• Analyze support calls: Gauge sentiments expressed in conversations to determine if customers are truly satisfied with the solutions provided.

• Auto-score all conversations: Evaluate all interactions based on your organization’s rubrics, allowing for improved sampling and quality assessment.

• Provide automatic dispositioning: Save agents time by automating the categorization of support calls.

• Tag coachable moments: Automatically identify and tag key moments in conversations to facilitate coaching and training.

• Generate VoC data: Analyze calls to provide Voice of the Customer insights without needing to send out surveys.

These are but a few of the potential uses of automated quality assurance software. Below, we illustrate such uses with our own software, Level AI, and then we go on to describe alternative AQM solutions that you may encounter in your research.

1. Level AI: best automated quality management software

Level AI is automation software for QA teams that understand the deeper meaning of language within the context of an entire conversation (as opposed to keyword-based systems that identify specific phrases in isolation). We call this conversational intelligence, and it helps businesses uncover valuable insights, improve quality management processes, and elevate the overall customer experience.

Here’s how we designed Level AI to automate parts of your quality assurance workflows so you can focus on higher-value tasks that improve overall customer service and drive continuous improvement within your organization:

Feature 1: understanding intent through semantic recognition

Many artificial intelligence quality management systems still rely on keywords to understand the content of support interactions. While detecting the presence of keywords in conversation has its uses, it’s not an ideal way to capture the full context and nuances of human language. Keywords can miss subtle variations in phrasing, tone, and intent, leading to incomplete or inaccurate assessments of customer interactions.

For example, given a statement by a customer who’s frustrated with a login issue, “The system doesn’t recognize my email address,” a keyword-based system might entirely miss such a statement, unless you’ve foreseen such a scenario and specifically added keywords targeting this.

A keyword system requires you to add tens or even hundreds of keywords around potential scenarios in advance to ensure the software identifies as many occurrences of these scenarios as possible.

Level AI leverages semantic intelligence and natural language understanding to accurately recognize such expressed intents — even those that are worded wildly differently — without the need to predefine all possible phrases and keywords in advance.

Our scenario engine identifies phrases that hint at a certain intent (or “scenario” as we call it), and applies conversation tags to these — like Greeting, Hold, or Language Barrier — highlighting these intents throughout the conversation.

These scenarios remain tagged and accessible, so that you can go back and review conversations where, for instance, customers had login issues.

Conversation tags are searchable and filterable, allowing you to create curated lists of conversations where certain intents were expressed:

As previously suggested, each scenario can have multiple conversation tags associated with it. In fact, you define scenarios that are relevant to your business and train each scenario with example phrases, as shown here:

In contrast to other systems that similarly let you set up scenarios, however, we use a semantic intelligence model that understands the meaning behind words and captures their variations. To further fine-tune the AI, we also show you “near-miss” phrases from similar conversations in the past that almost match the intent, which you can add or reject.

Feature 2: sentiment analysis to gauge customer feelings

Using technology to gauge the emotional undertones of conversations has always been challenging, but our machine learning quality management system software can discern emotions beyond simply being broadly positive or negative.

We identify, tag, and label specific phrases in any customer interaction — uttered by either agent or customer — as denoting a certain sentiment. We refer to these as sentiment tags.

Level AI currently recognizes the following sentiment tags:

• Anger

• Disapproval

• Disappointment

• Worry

• Happiness

• Admiration

• Gratitude

Examples of expressions of disappointment, as indicated by sentiment tags, are shown below:

Sentiment tags are particularly useful for reporting, since they’re searchable and filterable (like conversation tags) and allow you to report on all instances where customers were disappointed.

Besides identifying the emotions occurring inside a conversation, our solution also analyzes the overall feeling of an interaction. It does this by adding up the individually expressed sentiments according to when they occur, assigning a global sentiment score from 0 to 10 — with 0 being strongly negative and 10 being strongly positive.

This score isn’t a simple average but a weighted calculation that gives more importance to certain parts of the conversation, ensuring a more accurate reflection of the customer's overall experience:

You’ll notice that the conversation on the left has a lower sentiment score than the one on the right, although the customer expressed happiness at one point during both. This is because we put greater emphasis on sentiments expressed later in the call, since these were expressed after a potential resolution.

For example, if a customer calls frustrated about being overcharged and the agent corrects the bill and offers a small credit as compensation, any satisfaction expressed after the resolution indicates a successful outcome, regardless of the initial frustration.

However, if the customer is angry at the end of a call, this suggests they’re not happy with the agent or with the resolution.

Both these features (sentiment tags and the AI sentiment score) allow you to accurately measure and track the emotions of your customers to yield better insights into their behavior, which helps you provide better responses and proactively solve recurring issues.

Feature 3:automated quality scoring and feedback

Automated quality scoring and feedback assess how well customer service agents are performing. InstaScore, our auto scoring tool, rates how agents handle calls or chats and scores them based on predefined rubrics to give you immediate feedback on their performance.

InstaScore is expressed as a percentage and provides a quick overview of how well the system assesses an agent's performance according to a given rubric, such as:

• Did the agent greet the customer appropriately?

• Did the agent clarify and confirm the customer's concerns?

• Did the agent follow proper procedures to resolve the issue?

• Did the agent thank the customer for calling and offer further assistance?

In essence, InstaScore allows you to automatically score all customer interactions to identify those that require further scrutiny. This auto-scoring feature streamlines your workflows and automates time-consuming aspects of quality management, which would typically involve listening to entire calls.

Lastly, InstaScore is also useful for continuous improvement, as giving customer reps access to this data lets them evaluate their own performance and improve where needed.

Feature 4: automatic call dispositioning

Our semantic engine identifies the topics and issues discussed during interactions and automatically assigns relevant categories and subcategories to each.

This is helpful since agents often struggle with dispositioning right after a conversation due to post-call fatigue or inaccurate category descriptions.

Level AI creates its own categories after analyzing calls, but you can also create your own by providing a few example phrases, similar to creating your own scenarios.

Once an interaction is categorized, you’ll see the assigned category and subcategory in the conversation dashboard on the upper right-hand side:

Feature 5: instant evaluations for continuous improvement

Another aspect of QA is listening to calls to identify specific characteristics like requiring help from other agents or supervisors, having a longer-than-expected duration, or containing certain negative metrics.

This is one reason why some QA activities can be time-consuming. To address this, we offer coaching tools like InstaReview, which analyzes conversations and automatically flags those exhibiting negative characteristics.

Our platform allows you to easily search for and find conversations that have been InstaReviewed for closer scrutiny and training purposes.

We flag such conversations using color-coded tags:

Highlighting interactions in this way saves you time and effort from having to sample based on simple metrics like duration. Not only does InstaReview analyze all conversations, but it also processes more specific metrics like number of assists.

InstaReview tags can also serve as important reporting dimensions, and are searchable and filterable.

In addition to InstaReview, our platform offers dedicated Coaching dashboards, where you can identify key areas for agent improvement, track their progress over time, and provide targeted feedback:

Feature 6: Voice of the Customer (VoC) insights

Traditional VoC methods and data focus on specific metrics like CSAT, NPS, and customer effort, often gathered via post-interaction surveys. These surveys typically suffer from low response rates and inherent human biases.

Level AI analyzes the meanings behind all conversations to derive such metrics, without the need for surveys. It also identifies patterns and trends across all interactions that you might not have been aware of.

Examples of such patterns include a certain percentage of issues revolving around “Shipping delays” and “Warranty claim submissions.”

Even though customers express issues differently, Level AI recognizes trends and expresses these in standardized, descriptive ways.

This not only saves you the effort of manually compiling VoC data, but provides an accurate, unbiased view of customer sentiment. Furthermore, it can reveal unforeseen issues, like product defects, that you may not have previously detected.

Feature 7: comprehensive reporting and analytics

Our platform allows you to mix and match data from many different sources in order to find answers to questions like:

• What common issues lead to repeat calls from the same customers?

• What specific phrases or words spoken by agents are associated with high customer satisfaction scores?

• Are there any recurring issues that significantly affect first-call resolution rates?

• How do service issues differ between peak and off-peak hours?

• How does agent response time impact overall customer sentiment?

Level AI’s flexible reporting allows you to integrate data from disparate sources (e.g., your CRM, knowledge base, QA and survey tools) into cohesive analytics dashboards that provide unified views about your customers and your operations.

Our Query Builder enables you to create charts and dashboards with spreadsheet-like features, such as conditional and measurement operators.

You can also filter data using conversation tags and sentiment tags, among others.

Once created, you can specify who has access to reporting, and share reports with others via email. Level AI’s roles and permissions functionality means you can give users certain rights to the platform and independently give them access to specific sets of data — so managers can fully access the platform but only see data relevant to their team or group.

Getting Started with Level AI

Level AI helps you scale your quality management workflows by analyzing more calls with the same staff and resources. It auto-scores interactions to reveal gaps in customer service and operational efficiency.

Schedule a demo

Getting Started with Level AI

Level AI helps you scale your quality management workflows by analyzing more calls with the same staff and resources. It auto-scores interactions to reveal gaps in customer service and operational efficiency.

Schedule a demo

2. Verint

Verint is an analytics company that produces customer experience automation software for large enterprises and banks. They offer a suite of AI-powered bots for analyzing and auto-scoring customer interactions.

Features

• Verint’s AQM platform features three types of quality bots:

• Quality template bot — Uses generative AI to help you set up scoring rules by learning from your rubrics and previously provided examples.

• Performance scoring bot — Auto scores your customer interactions according to your scoring rules and auto assigns coaching based on a particular agent’s KPIs.

• Compliance scoring bot — Evaluates your customer interactions for adherence to internal and external policies.

Pricing

Verint doesn’t disclose pricing on their website; contact their team for a custom quote.

3. Observe.AI

Observe.AI provides automated quality management software for contact centers, which they refer to as Auto QA. They give contact centers visibility into all customer conversations to provide management with high-level awareness of what’s going on.

Features

Observe.AI’s auto QA features provide:

• Customer interaction analysis: AI analyzes 100% of customer interactions to boost revenue, enhance the customer experience, and reduce compliance risk.

• Agent coaching: Builds personalized coaching plans using performance insights and scales personalized coaching.

• Automation rules: Generative artificial intelligence creates flexible metadata, automation rules, and advanced conditions to improve business decisions.

Pricing

Observe.AI doesn't list pricing on their website. You must book a demo for a quote.

4. Zendesk

Zendesk is a quality management software solution for contact centers that offers a suite of different products around customer service, such as a CRM, a ticketing system, a database, a sales module, and more.

For customer service QA, the company purchased Klaus, which was a provider of AI-powered automated quality management system software. Today this is known as Zendesk QA.

Features

Zendesk QA:

• Comprehensive conversation analysis: Highlights gaps in customer service and identifies coaching opportunities for agents.

• Internal quality reviews: Tracks quality at the agent, team, and company levels.

• Survey design assistance: Helps create surveys for collecting VoC data.

• Sentiment analysis: Determines whether customer sentiment is positive or negative.

• Customizable scorecards: Allows you to build scorecards tailored to the needs of your business.

Pricing

Zendesk QA’s pricing starts at around $35 per agent per month.

5. Enthu.AI

Enthu.AI is primarily a sales coaching tool for customer service representatives but also provides cloud features for automating quality management, including real-time coverage of customer conversations and service quality.

Features

Enthu.AI offers the following AQM functionality:

• Speech-to-text transcription: Converts speech to text for analyzing customer conversations in various global English dialects.

• Key moment identification: Identifies important moments in calls with a user-friendly interface for detailed analysis.

• Call filtering: Allows filtering by date, agent, moment, feedback, and more, to create curated lists for better sampling.

• Compliance rating and evaluation: Facilitates the rating and evaluation of calls for compliance issues, allowing you to share these ratings with teammates.

Pricing

Enthu.AI doesn’t give out their exact pricing, but according to their website, you pay by number of seats, and whether or not you’re using artificial intelligence features.

6. MaestroQA

MaestroQA is a quality management platform built for customer support teams that want to combine AI scoring with structured human review. It’s widely used by digital-first brands that run support through helpdesk tools like Zendesk, Salesforce and Kustomer.

MaestroQA’s strength is QA workflow management. Teams can build custom rubrics, assign reviews, run calibration sessions and connect QA results to coaching, all in one place. Its AI AutoQA feature uses large language models to evaluate interactions against custom rubrics and flags lower-confidence scores for human calibration, which suits organizations that want AI to reduce manual workload without removing reviewers from the loop. It also offers reporting that ties QA scores to CSAT, resolution rates and other CX metrics. However, because the platform grew out of manual QA, teams looking for a fully automated, intent-driven approach may find its human-review workflows more central than they need

Key features

• AI AutoQA: Scores interactions against your custom rubrics using LLMs.

• Calibration workflows: Flags low-confidence AI scores and aligns reviewers on grading standards.

• Coaching tools: Connects QA findings to agent coaching sessions and feedback.

• Helpdesk integrations: Pulls tickets and conversations from popular support platforms.

• CX reporting: Links QA scores to CSAT, resolution rates and other outcomes.

Best suited for

Customer support teams on helpdesk platforms that want to keep a meaningful role for human QA reviewers while automating part of the scoring.

Pricing

MaestroQA doesn’t publish pricing on its website; you need to contact sales for a quote.

7. CallMiner Eureka

CallMiner Eureka is an enterprise conversation analytics platform that analyzes customer interactions across voice and digital channels. It’s known for deep speech analytics, automated scoring and compliance monitoring in large contact centers.

CallMiner has been one of the long-standing names in speech analytics, and Eureka is its core platform for turning interaction data into insights. It transcribes and analyzes calls, chats, emails and other channels, then applies scoring to evaluate agent performance, compliance and customer experience at scale. Its real-time capabilities can surface alerts and guidance while conversations are happening, and its coaching tools help managers act on what the analytics reveal. The trade-off is complexity: CallMiner is powerful, but much of its categorization relies on configured rules and queries, so teams often need dedicated analysts to get full value.

Key features

• Speech and text analytics: Analyzes 100% of interactions across voice and digital channels.

• Automated scoring: Scores conversations for performance, compliance and customer experience.

• Real-time alerts: Surfaces risk, compliance and sentiment events during live calls.

• Coaching tools: Turns analytics findings into agent coaching opportunities.

• Redaction: Removes sensitive data from recordings and transcripts.

Best suited for

Large enterprises with dedicated analytics teams that need deep conversation analytics and compliance monitoring across high interaction volumes.

Pricing

CallMiner offers custom pricing based on interaction volume and modules.

8. evaluagent

evaluagent is a quality assurance and agent engagement platform that combines automated QA, conversation intelligence and coaching. It’s popular with mid-sized contact centers and BPOs that want to improve agent performance and engagement together.

evaluagent focuses on closing the loop between quality and improvement. Its auto-QA scores interactions automatically, while QA teams can still run manual evaluations and calibration where needed. The platform links quality results to coaching workflows and uses gamification to keep agents engaged with their own performance. Its conversation intelligence features add sentiment analysis and topic discovery on higher tiers. For teams that want an approachable QA platform with transparent pricing, it’s a solid option, though its real-time capabilities and analytics depth are lighter than enterprise suites.

Key features

• Auto-QA: Automatically scores interactions against your quality criteria.

• Conversation intelligence: Adds sentiment analytics and topic discovery on higher plans.

• Coaching workflows: Connects QA results to targeted coaching and improvement plans.

• Gamification: Motivates agents with performance-based engagement tools.

• Calibration: Keeps evaluators aligned on scoring standards.

Best suited for

Mid-sized contact centers and BPOs that want automated QA, coaching and agent engagement in one platform with predictable pricing.

Pricing

According to evaluagent’s own pricing comparison, its AutoQM & Improvement plan starts from $35/user/month with 100% auto-scoring, coaching workflows and gamification, while AutoQM + Conversation Intelligence starts from $65/user/month and adds sentiment analytics, xNPS, topic discovery and Spotlight.

9. NICE CXone

NICE CXone (now branded CXone Mpower) is an enterprise cloud contact center platform with built-in quality management, workforce management, interaction analytics and AI. It’s one of the most widely deployed CCaaS platforms in regulated industries like banking, insurance and healthcare.

NICE’s advantage is breadth. Quality management, recording, analytics, workforce management and AI all live on one platform, so organizations already running NICE can automate QA without adding another vendor. Its Enlighten AI analyzes interactions for agent soft skills and behaviors and can auto-score conversations, while Interaction Analytics surfaces operational trends across every interaction. Copilot gives agents real-time guidance and automatic summaries. The downside is that NICE is complex to configure, many AI capabilities are licensed separately, and the platform delivers the most value only when you commit to the full NICE ecosystem.

Key features

• Quality management: Evaluation forms, auto-scoring and calibration within the CCaaS platform.

• Enlighten AI: Analyzes agent soft-skill behaviors across every interaction.

• Interaction analytics: Identifies trends in operations, agent performance and customer experience.

• Copilot: Real-time agent assistance and AI-generated interaction summaries.

• Workforce engagement: Integrated workforce management and performance management.

Best suited for

Large enterprises, especially in regulated industries, that want quality management, recording, analytics and workforce management on a single CCaaS platform.

Pricing

NICE offers custom quotes, and quality management, AI and analytics modules are typically priced on top of core CCaaS licenses.

Who offers the best automated QM in call center software (AI scoring, QA automation)?

The best automated QM platforms score 100% of interactions against your own rubric, explain why each score was given, and let QA teams calibrate the AI over time. Level AI leads here because its InstaScore uses semantic intelligence to understand intent, not just keywords, so it scores nuanced conversations accurately without requiring you to predefine hundreds of phrases. Every conversation is scored automatically, and InstaReview flags the interactions that need human attention.

Among the alternatives, NICE CXone and Verint offer automated scoring inside large enterprise suites, which suits organizations already committed to those ecosystems. Observe.AI and CallMiner provide strong auto-scoring alongside conversation analytics. MaestroQA and evaluagent combine AI scoring with human review and calibration workflows, while Zendesk QA is the natural choice for teams already on Zendesk.

Which companies have the best AI for intelligent QA, sentiment analysis, and real-time coaching?

Look for three things: AI that understands intent, sentiment analysis that goes beyond positive and negative, and coaching that happens during the call rather than days later. Level AI covers all three. Its sentiment analysis detects specific emotions like anger, disappointment, worry and gratitude, then weights sentiment toward the end of the call to reflect how the customer felt after resolution. For real-time coaching, Real-Time Agent Assist gives agents live prompts and answers through AgentGPT, while managers can monitor calls live and step in with whisper or barge.

NICE CXone offers real-time guidance through Copilot and soft-skill analysis through Enlighten AI. CallMiner provides real-time alerts for compliance and sentiment events. Observe.AI combines sentiment analysis with coaching, while evaluagent and Zendesk QA include sentiment analysis but focus mainly on post-interaction coaching.

What’s the best software to replace manual QA scorecards in contact centers?

The right replacement should take your existing scorecard, apply it to every interaction automatically, and keep humans involved only where judgment matters. Level AI’s Auto-QA does exactly this: you bring your rubric questions (such as “Did the agent verify the customer’s identity?” or “Did the agent offer further assistance?”), and InstaScore grades 100% of conversations against them. QA analysts then review flagged interactions, calibrate scoring and focus on coaching instead of filling out forms.

If you want to transition gradually, MaestroQA and evaluagent let you run AI scoring alongside manual evaluations and calibrate the two. For teams on Zendesk, Zendesk QA offers customizable scorecards within the same ecosystem. Enterprises using NICE CXone or Verint can automate scorecards inside their existing workforce engagement suites.

Start automating quality assurance with Level AI

Level AI offers:

• 100% auto-scored call center quality to ensure every conversation is scored consistently, eliminating the inefficiencies and biases of manual QA.

• Auto-reviews of agent performance to quickly and accurately highlight gaps and coachable opportunities.

• Automatic intent recognition using natural language understanding and semantic intelligence to tag contextually relevant conversational moments.

• VoC insights that are generated from conversations, not surveys, to reveal valuable and non-obvious customer feelings.

• Flexible speech analytics and reporting tools for a unified view of agent performance and customer feedback.

Get a free demo today!

Auto-score every interaction, uncover coaching moments instantly, and give your QA team time back for what matters.

Schedule a demo

Get a free demo today!

Auto-score every interaction, uncover coaching moments instantly, and give your QA team time back for what matters.

Schedule a demo

Frequently asked questions