Enterprise Call Quality Monitoring Software: 10 Best Tools for 2026 (Updated List)

Introduction

They track customer interactions in real time and alert call center managers about instances where agents aren’t performing at their best. After calls are completed, these tools assess agent output against company rubrics, helping teams pinpoint specific areas for improvement.

Below, we’ll break down three key factors to consider when evaluating call monitoring tools and highlight eight platforms that offer these capabilities, including our own customer experience and QA automation solution, Level AI.

What are key factors to consider when choosing a call quality monitoring tool?

To get real value from a call quality monitoring tool, it must capture and analyze conversations both fully and accurately in real time. That means understanding the meaning and sentiment of conversations, not just looking for specific words.

Just as importantly, post-call analysis should account for the full context of each conversation and assess agent performance based on clear, predefined criteria.

Below, we’ll explore the three most important factors to consider when evaluating call monitoring solutions.

Does this solution capture customer intent or only words?

Some call monitoring tools rely on keyword matching to detect customer intent, but this approach can be inaccurate because it ignores the full context of the conversation. Instead, it flags complete or partial matches from a predefined list of words assumed to represent specific intents.

QA teams must also try to anticipate every possible way a customer might express a need. For instance, a single intent like “product return” could show up as “send back,” “give back,” “return this,” or countless other variations.

A more effective approach uses AI to analyze conversations in real time. These systems don’t just detect words; they interpret meaning. By understanding context and natural language, they can accurately identify what the customer truly wants. We’ll show examples of this below.

Does this solution take into account customer sentiment?

To get an accurate picture of customer experience, it’s just as important to track how your customers feel about their interactions as it is to calculate the usual metrics like average time to resolution.

For example, if an agent has been impolite, a customer may still feel upset, even if their question has been answered quickly. Customer sentiment analysis gives extra information that regular call center metrics might miss. It helps explain how agents are really doing by showing how customers feel during and after a conversation.

When you look at sentiment data from many calls, it becomes even more useful. If a lot of customers are upset about the same issue, like a confusing process or a product problem, it can point to a bigger problem that needs fixing. On the other hand, if customers express positive sentiments after talking to certain agents, that can show what those agents are doing well, so others can learn from them.

Can the solution auto-score agent performance against predefined criteria?

Support teams generally don’t have time to listen through the entirety of every customer call, usually getting through only about 1–2% of them. Since these calls are typically randomly sampled, they may not show a full picture of how agents are doing, which can lead to missing important problems or overlooking what’s working well.

That’s why it’s important to find a call quality management tool that can automatically score every customer conversation using your company’s own guidelines. A good tool should also point out which calls need closer attention from the QA team. This way, the team can spend their time on the calls that matter most, like coaching agents, spotting problems, and improving service.

Read on to learn more about eight top customer support solutions that support call quality monitoring, starting with our platform, Level AI.

Comparing call quality monitoring tools

Before you commit to any single platform, it helps to see all ten call quality monitoring tools side by side: who they're built for, what makes each one different, and how real users rate them on G2. For a broader look at how these fit into the wider category, this rundown of AI quality management software is a useful next read.

10 Best Call Quality Monitoring Tools for Contact Centers in 2026

Ten platforms compared side by side: what each one does, who it's actually built for, and what real users say on G2.

Call center leaders are under more pressure than ever to catch problems before they show up in churn numbers or a compliance fine. The old way of running call quality monitoring, manual sampling, only ever covered one to three percent of conversations, which means the vast majority of what happens between agents and customers goes unseen. That gap is exactly why the market for call quality monitoring tools has gotten so crowded, and why picking the right one for your team's size and stack matters.

This guide compares ten call center monitoring software platforms: dedicated QA automation vendors, CCaaS suites with QA bolted on, and a couple of sales conversation intelligence tools that get mentioned in the same breath even though they solve a different problem. For each one, we cover the core features, who it's actually built for, and what real users say on G2. If your team is still relying on spot checks and spreadsheets, a platform built specifically for quality assurance can score every interaction instead of a small sample, which is the main thing to look for when you're comparing call center call monitoring options.

1. Level AI

Level AI is a contact center intelligence platform that automates quality assurance across 100 percent of voice, chat, and email conversations, replacing the 1 to 3 percent most teams manually sample. It pairs automated QA with real-time Agent Assist, AgentGPT for instant knowledge base answers, conversational analytics, and Inferred CSAT, which scores customer sentiment on every interaction rather than only the few who respond to a survey. Built on a proprietary AI stack trained specifically for contact center language, Level AI is used by mid-market and enterprise teams that have outgrown manual sampling or are replacing legacy tools like Verint.

Features

• Automated QA that scores 100 percent of voice, chat, and email conversations against custom rubrics, instead of the 1 to 3 percent most teams manually sample

• Real-time Agent Assist that surfaces knowledge base articles, next-best-action prompts, and compliance alerts while a call is still live

• AgentGPT, a generative AI assistant agents can ask questions in plain language to pull answers straight from the knowledge base

• Conversational intelligence and Voice of the Customer analytics that surface call drivers, sentiment, and emerging trends without manual tagging or keyword lists

• Agent screen recording tied directly to QA scores, for compliance and process adherence

• Inferred CSAT (ICSAT), which estimates customer sentiment on every interaction rather than just the small share of customers who respond to a post-call survey

Best for

Level AI is built for mid-market and enterprise contact centers, particularly teams replacing a legacy QA and workforce management stack. Companies that have outgrown manual quality sampling, or that are moving off tools like Verint for QA and analytics while keeping them for workforce management, are the most common fit. Its proprietary AI platform, built with models trained specifically for contact center language rather than a repurposed general-purpose model, is often the deciding factor for teams that tried a generic AI layer first and found the accuracy wasn't there.

Product reviews

4.7 / 5~200 verified reviews

Customers often talk about how easy Level AI is to implement and how much depth they get from its reporting.
One reviewer called it reliable and efficient and said it continues to exceed expectations. Level AI was also recognized as a 2026 G2 Best Software Award winner in the Agentic AI Products category.

2. Verint

Features

• Automated quality management that scores calls, chats, and screen activity against configurable criteria

• Workforce management (WFM) for forecasting, scheduling, and adherence tracking

• Speech and text analytics for compliance monitoring and root cause analysis

• Screen and call recording for compliance archiving

• Desktop and process analytics that track application use during a call

• Reporting and dashboards across the interaction analytics suite

Best for

Verint is built for large, often regulated enterprises that need workforce management and compliance recording at scale. Plenty of contact centers keep Verint for WFM specifically while looking elsewhere for QA. Teams working through a Level AI vs. Verint comparison usually cite Verint's manual QA workflows and dated interface as the reason they started looking in the first place, even as they keep it for scheduling.

Product reviews

4.5 / 5 from 41 reviews (Automated Quality Management);
4.4 / 5 across 953 reviews company-wide.

Users like the strong reporting and the ability to evaluate both voice and text interactions. Some users mention that the setup took longer than expected, while others note that creating custom reports can require SQL knowledge.

3. Talkdesk

Features

• Native CCaaS platform combining voice, digital channels, and workforce engagement management in one system

• Built-in auto QA that scores a sample of interactions against configurable scorecards

• Real-time and post-call speech analytics for sentiment and keyword spotting

• IVR and omnichannel routing built into the same platform as the QA tools

• Workforce management for scheduling and forecasting

• AI-generated call summaries and CRM integrations that cut down after-call work

Best for

Talkdesk fits mid-market and enterprise teams that want telephony, routing, and QA under one contract instead of running a separate call monitoring software layer. It's a reasonable starting point for teams that haven't invested in dedicated QA before. Where it tends to fall short is granularity at scale: one contact center evaluating Talkdesk's native auto QA transcription tool was quoted close to a hundred thousand dollars a year, which pushed them to look at dedicated QA platforms instead.

Product reviews

4.4 / 5 from 2,567+ reviews, with 94% rated 4–5 stars.

Users consistently praise Talkdesk for its ease of use and quick implementation. Reviewers also highlight its computer telephony integration and reporting dashboards as some of its strongest features.

4. Zendesk

Features

• Full ticketing, live chat, and omnichannel case management suite

• Zendesk QA (formerly Klaus) for scoring conversations against custom rubrics

• AI-powered auto QA for chat and ticket interactions

• Built-in CSAT surveys attached to every resolved ticket

• Workforce management add-on for scheduling and forecasting

• Reporting dashboards spanning tickets, channels, and individual agents

Best for

Zendesk QA suits SMB and mid-market support teams that are already running Zendesk as their ticketing and CRM system and want a bolt-on QA layer rather than a separate platform. It's a natural fit if most of what you're reviewing is chat and ticket interactions rather than phone calls. Larger teams evaluating it for phone-heavy QA often find the AI scoring and qualitative feedback tools thinner than what a dedicated platform offers, and some end up looking at Level AI as a more complete alternative once they hit that ceiling.

Product reviews

4.3 / 5 from 7,142+ reviews, with 90%+ rated 4–5 stars.

Users generally rate the platform highly, but the most common concern among 4-star reviews is that the pricing and complexity can increase as usage scales.

5. Dialpad

Features

• Cloud telephony (UCaaS/CCaaS) with AI features built into the calling layer

• Real-time transcription during live calls

• AI-generated sentiment analysis and an AI-driven CSAT score

• Auto-generated QA scorecards on every call

• Supervisor tools for live monitoring, whisper coaching, and barge-in

• Native CRM integrations with platforms like Salesforce and HubSpot

Best for

Dialpad works well for SMB and mid-market teams that want phone infrastructure and a first pass at call center call monitoring in the same bill, without a separate analytics contract. Its built-in real-time speech analytics flags sentiment as calls happen, but the auto-generated QA scorecards tend to weigh customer tone more heavily than whether the issue actually got resolved, so teams with more specific scoring needs often pair it with, or replace it with, a dedicated QA tool.

Product reviews

4.4 / 5 from ~4,900 reviews.

Users often praise the platform for being easy to use and say support is quick when it comes to basic issues. Some reviewers, however, mention that support can be slower and less responsive when dealing with more complex or ongoing problems.

6. CallMiner

Features

• Speech and text analytics engine (CallMiner Eureka) for keyword and category-based analysis across large call volumes

• Automated compliance monitoring and risk detection

• Category-based scoring for QA and coaching

• Real-time guidance surfaced to agents during calls

• Dashboards and reporting built for high interaction volumes

• Integrations with major CCaaS and telephony platforms

Best for

CallMiner is best suited to large enterprises with a dedicated analytics team that can maintain keyword libraries and category taxonomies over time. It's a mature platform for compliance-heavy industries, but its keyword-based approach to categorization takes ongoing manual tuning and can miss context that intent-based AI would catch. Teams researching a CallMiner alternative are usually trying to move off keyword lists toward AI that understands intent without constant maintenance.

Product reviews

4.5 / 5 from 223 reviews, with 73% rated five stars.

Users praise the depth of the analytics and the quality of support. Some reviewers, however, say there is a steep learning curve, particularly when it comes to the platform's query syntax.

7. Observe.ai

Features

• Automated QA scoring across 100 percent of voice and digital conversations

• Generative AI call summaries and automatic tagging

• Real-time agent assist with prompts during live calls

• Coaching workflows tied directly to QA scores

• Sentiment and root cause analytics across the full interaction volume

• Integrations with major CCaaS platforms and CRMs

Best for

Observe.ai comes up often on startup and mid-market shortlists, partly because it competes hard on price to win deals. It's a reasonable option for teams that want automated QA without enterprise-level spend. Teams running a Level AI vs. Observe.ai evaluation tend to point to differences in contextual understanding and intent-level scoring as the deciding factor once call volume grows.

Product reviews

4.6 / 5 from 238 reviews, with 97% rated 4–5 stars.

Users like the clean interface and how quickly the platform surfaces coaching opportunities. The most common complaint is around transcription accuracy, especially with noisy audio or heavier accents.

8. Enthu.AI

Features

• Automated call scoring against custom QA scorecards

• Conversation intelligence with searchable transcripts and moment tagging

• Coaching workflows tied to individual agent scorecards

• Sentiment analysis across calls

• Team and agent-level QA trend dashboards

• Integrations with common CCaaS and helpdesk tools

Best for

Enthu.AI fits small and mid-sized teams that want an affordable, quick-to-set-up QA layer without a long implementation cycle. It tends to show up on shortlists alongside sales-focused conversation tools rather than only other customer support QA tools, since prospects often evaluate it in the same round as platforms built for sales teams.

Product reviews

4.9 / 5 from 41 reviews.

Users consistently praise the platform for being easy to use and accurate when it comes to transcription. Several reviewers also say they found it easier to do business with compared to other platforms.

9. Gong

Features

• Conversation recording and transcription across calls, video meetings, and email

• Revenue intelligence dashboards tracking deal risk, talk ratios, and competitor mentions

• Deal and pipeline forecasting built from conversation data

• Coaching insights based on rep talk patterns and adherence to sales best practices

• CRM integrations with Salesforce and HubSpot that auto-log activity

• AI-generated call summaries for sales teams

Best for

Gong is built for mid-market and enterprise sales organizations, not contact center QA teams. It's genuinely one of the stronger conversation intelligence platforms for go-to-market use cases like deal risk and rep coaching, but it doesn't automate scorecard completion or produce per-agent adherence scores the way a contact center QA platform does. Teams that need both sales conversation intelligence and contact center QA typically run Gong alongside a dedicated QA tool rather than asking it to do both jobs.

Product reviews

4.7 / 5 from 6,600+ reviews, ranked #1 in Revenue Operations & Intelligence.

Users praise the depth of the coaching and forecasting insights. The main concern that comes up is pricing, with some reviewers describing it as expensive and not always transparent.

10. Medallia

Features

• Post-interaction CSAT, NPS, and FCR survey collection across channels

• Text and sentiment analytics on open-ended survey responses

• Case management workflows for closing the loop on detractor feedback

• Digital experience analytics for web and app journeys

• Employee experience surveys alongside customer feedback

• Enterprise-wide dashboards and reporting for VoC programs

Best for

Medallia is built for large enterprises running structured voice of customer programs across many locations, generally organizations with 5,000 or more employees. It's strong at collecting and centralizing survey data at scale, but its QA process tends to stay manual, and because it depends on customers actually completing a survey, coverage of reviewed interactions is often lower than teams expect. Companies looking at Level AI as a Medallia alternative are usually trying to close that coverage gap by scoring every conversation instead of relying on the share of customers who respond to a survey.

Product reviews

4.5 / 5 from 628 reviews.

Users like how the platform brings large volumes of qualitative feedback into one place and automatically surfaces key themes. Some reviewers note that getting the most out of the platform can require specialized expertise, while others say the cost can be high, particularly for the employee experience program.

Why Level AI Is the Right Call Quality Monitoring Tool For You

Every other tool here solves one piece of call quality monitoring: telephony with QA attached, survey-based CSAT, or sales conversation intelligence that never touches a scorecard. Level AI does the whole job. It scores 100 percent of voice, chat, and email conversations instead of the usual 1 to 3 percent sample, pairs that coverage with real-time Agent Assist and Inferred CSAT, and runs on a proprietary AI stack built for contact center language, not a repurposed general model. And thats why enterprise and mid-market teams keep replacing legacy QA stacks with it.

Score Every Conversation Instead of a Sample

See how Level AI reviews 100 percent of your calls, chats, and emails and surfaces the coaching moments manual sampling misses.

Schedule a demo

Score Every Conversation Instead of a Sample

See how Level AI reviews 100 percent of your calls, chats, and emails and surfaces the coaching moments manual sampling misses.

Schedule a demo

Frequently asked questions