AI Call Quality Monitoring Tools for Contact Center in 2026

Call quality used to mean pulling a handful of recordings, listening to them manually, and hoping that sample was representative of what was actually happening across the contact center.

That gets harder once AI agents start handling more conversations. Teams now need to review not just whether a call was completed, but whether the agent understood the customer, followed the right workflow, handled transfers properly, stayed compliant, and gave a useful answer.

That is where AI call quality monitoring becomes useful. The better platforms can help teams review conversations at scale, surface failed interactions, track recurring issues, and improve agent performance without relying entirely on manual QA.

In this guide, we compare eight platforms worth evaluating in 2026, with a focus on how well they help contact-center teams monitor, test, and improve call quality once AI agents are actually live.

TL;DR

• Plivo is a strong all-round option for teams that want call quality monitoring close to the same platform running their AI agents and telephony.

• Vapi is best for developer-led teams that want flexible evaluations, simulations, and custom QA logic.

• Retell AI works well for teams that want built-in post-call analysis, scoring, and testing without building the QA layer from scratch.

• Bland AI is a good fit for evaluating large volumes of AI calls against custom quality criteria.

• Synthflow AI suits ops-led teams that want a more visual, less developer-heavy way to review and test conversations.

• ElevenLabs stands out when natural conversation quality is a major part of how you measure AI performance.

• Twilio is strong for enterprises that want to monitor both conversation quality and the technical performance of the underlying call.

• LiveKit is best for engineering teams that need deeper observability across recordings, transcripts, logs, traces, and latency.

For teams starting fresh, Plivo is one of the more balanced options to evaluate because it keeps AI agents, telephony, and call performance monitoring closer together instead of turning QA into another disconnected part of the stack.

What Actually Matters in AI Call Quality Monitoring

Once AI agents are live, call quality is not just about whether the conversation sounded natural. The more important question is whether the agent handled the interaction correctly, followed the expected process, and gave the team enough visibility to improve what happens next.

1. Call Recording and Transcripts

You need a reliable record of what happened on every call. Look for platforms that make recordings and transcripts easy to access, search, and review, so your team is not digging through logs just to understand why a conversation failed.

2. Automated Evaluations and Call Scoring

Manual QA does not scale well. A stronger platform should let you evaluate calls automatically against criteria like task completion, correct answers, transfer quality, script adherence, or whether the agent followed the right workflow.

3. Custom QA Criteria

Every contact center measures quality differently. The platform should let you define what a “good” call looks like for your business, rather than forcing every team into the same generic scorecard.

4. Sentiment and Conversation Analysis

Basic transcripts tell you what was said. Better monitoring tools help you understand how the conversation went, including customer sentiment, interruptions, repeated questions, awkward pauses, or moments where the interaction started to break down.

5. Compliance Monitoring

For regulated teams, quality monitoring also needs to catch risky behavior. That can include missed disclosures, incorrect handling of sensitive information, or conversations that do not follow internal compliance rules.

6. Call Summaries and Data Extraction

A good platform should make each conversation easier to review without listening to the full recording. Summaries, structured outcomes, extracted fields, and call dispositions can help teams quickly understand what happened and where follow-up is needed.

7. Testing and Simulation

The best time to catch a bad workflow is before a customer does. Simulation and testing tools make it easier to test different prompts, edge cases, transfers, and customer scenarios before changes are pushed into production.

8. Analytics and Debugging

Quality monitoring becomes much more useful when teams can see patterns across hundreds or thousands of calls. Look for dashboards and debugging tools that help identify repeated failures, low-performing flows, transfer issues, and other problems that would be easy to miss one call at a time.

A detailed Comparison Table of AI Call Quality Monitoring Tools

Plivo

Plivo is a strong fit for contact centers that want call quality monitoring built close to the same environment where their AI agents and phone calls already run. Teams can review conversations, transcripts, transfer behavior, resolution rates, and other performance signals without immediately adding a separate QA platform.

That is especially useful once AI agents move beyond a pilot. Instead of only checking whether calls are connected, teams can look at where conversations are failing, how often customers are being transferred, how long calls are taking, and whether changes to the agent are actually improving outcomes.

Key Strengths

• Conversation-level visibility: Teams can review individual AI conversations and use transcripts and call data to understand what happened during an interaction.

• AI performance monitoring: Reports track metrics such as resolved conversations, AI-to-human transfer rates, handle time, and conversation volume, helping teams spot changes in agent performance.

• Testing before production: Plivo includes a playground and testing tools so teams can check agent behavior and troubleshoot flows before exposing changes to real callers.

• Useful quality trends: Reporting helps teams see patterns across conversations instead of evaluating calls one at a time, which is useful for identifying recurring transfer or resolution issues.

• Call debugging close to the voice stack: Because the AI agent and telephony sit within the same broader platform, teams have fewer layers to investigate when a call does not behave as expected.

Limitations

• Not a dedicated workforce QA platform: Plivo is better suited to monitoring and improving AI-driven conversations than running traditional supervisor-led QA programs for large teams of human agents.

• Evaluation depth may not suit every QA program: Teams that need highly customized human-agent scorecards, coaching workflows, or specialized compliance QA may still need a dedicated quality-management product.

Pricing

Plivo Voice AI Agents cost $0.04 per minute, with a pay-as-you-go option and $10 in free credits for getting started.

Vapi

Vapi is a strong option for developer-led teams that want more control over how call quality is tested and monitored. Its monitoring and evaluation tooling goes beyond simply storing recordings, giving teams ways to automatically check conversations, test agent behavior before launch, and investigate why individual calls passed or failed.

Key Strengths

• Automated call quality monitoring: Teams can continuously monitor live AI calls against defined quality, effectiveness, and compliance criteria.

• Flexible evaluations: Vapi lets teams create custom evaluations for specific agent behaviors, workflows, and expected outcomes.

• Realistic pre-production testing: Simulations can run AI-powered test calls and produce recordings and transcripts, making it easier to catch issues before customers do.

• Detailed call artifacts: Recordings, transcripts, logs, and timing information give technical teams plenty of context when debugging a failed interaction.

• Custom call analysis: Teams can generate summaries, structured outputs, success evaluations, and other post-call data based on what they want to measure.

Limitations

• More developer-focused: Getting the most out of monitoring, evaluations, and custom analysis is easier if your team is comfortable working with APIs and configuration.

• QA setup requires more thought: Vapi gives teams a lot of flexibility, but you still need to define what should be monitored and what a successful conversation looks like.

• Not a traditional human-agent QA suite: It is primarily designed around monitoring and improving AI agents rather than supervisor-led coaching and workforce quality management.

• Multiple layers can complicate debugging: Because teams can use different telephony, speech, model, and voice providers, finding the source of a quality issue can sometimes involve several components.

Pricing

Vapi charges a $0.05 per minute platform fee, with telephony, transcription, LLM, and voice provider costs added separately. Based on the setup, a more realistic all-in cost can be around $0.13 to $0.33 per minute.

Retell AI

Retell AI is a good fit for teams that want call quality monitoring built into the same platform they use to run their AI voice agents. What makes it useful from a QA perspective is the combination of post-call analysis, production call scoring, live monitoring, and pre-launch testing, so teams can catch problems both before and after an agent goes live.

Key Strengths

• AI Quality Assurance: Retell can automatically score production calls for quality and accuracy, which makes it easier to review performance across a larger volume of conversations.

• Structured post-call analysis: Teams can extract specific outcomes from every call, such as whether an issue was resolved, the reason for the call, urgency, satisfaction, or other custom fields.

• Live call monitoring: Supervisors can follow live transcripts, listen to active calls, and step in when needed, which is useful for spotting problems that should not wait until post-call review.

• Simulation and regression testing: Teams can create graded test cases and rerun them after changing prompts or workflows to check whether a fix introduced new problems.

• Custom quality dashboards: Retell lets teams build dashboards around the call metrics and outcomes that matter to their operation rather than relying only on a standard report.

Limitations

• Focused mainly on AI-agent QA: Retell is strong for evaluating its own AI conversations, but it is not a traditional workforce quality-management platform built around coaching and scoring large teams of human agents.

• Good QA still depends on good criteria: Automated scoring becomes useful only after teams clearly define what a successful call, failed call, or acceptable response should look like.

• Some analysis needs configuration: Custom outcomes and QA fields have to be designed around your workflow, so useful monitoring is not completely automatic out of the box.

• Best suited to Retell-based deployments: If most of your contact-center calls run through another voice platform, Retell is less compelling as a standalone monitoring layer.

Pricing

Retell AI starts from $0.07 to $0.31 per minute for AI Voice Agents, with call analytics, transcripts, simulation testing, and platform access included. Enterprise deployments can move to custom pricing based on scale and requirements.

Bland AI

Bland AI is a good fit for teams running large volumes of AI calls and wanting a more structured way to catch quality problems without manually reviewing everything. Its monitoring stack now goes beyond basic call logs, with dedicated evaluations, detailed call metrics, test simulations, and alerts that help teams understand both individual failures and broader QA trends.

Key Strengths

• Dedicated call evaluations: Bland can score real production calls against custom quality criteria such as tone, hallucinations, issue understanding, and resolution.

• Detailed call-level metrics: Call logs surface signals such as transcription quality, response time, silence, sentiment, engagement, repetition, interruptions, and speaker changes.

• Audio-aware analysis: Bland can evaluate the actual call audio, not just the transcript, which helps surface issues around tone, background noise, and other voice-specific quality problems.

• Simulation before changes go live: Teams can test specific conversation scenarios, define success criteria, and review the resulting transcripts before rolling changes into production.

• Quality alerts: Teams can flag important call conditions and, on Enterprise plans, monitor deeper signals such as latency, sentiment, silence, transcription quality, engagement, and interruptions.

Limitations

• Most useful for Bland-based call operations: The monitoring experience is built around calls running through Bland, so it is less compelling as a standalone QA layer for calls handled elsewhere.

• Advanced QA capabilities can require more setup: Custom evaluation agents, rubrics, benchmarks, and alerts are powerful, but teams still need to define what good and bad performance looks like.

• Some deeper monitoring is Enterprise-focused: Advanced metrics, alerts, and certain analysis capabilities are tied to larger deployments.

• Not designed as a human-agent coaching suite: Bland is better suited to evaluating AI conversations than managing traditional supervisor coaching and workforce QA programs.

Pricing

Bland AI starts at $0.14 per minute, with higher tiers and Enterprise plans available for larger call volumes and more advanced monitoring requirements.

Synthflow AI

Synthflow AI is a good fit for teams that want call quality monitoring without turning QA into a developer-heavy project. It combines call logs, custom evaluations, analytics, and automated testing in a more visual environment, making it easier for ops teams to see where AI conversations are working and where they need attention.

Key Strengths

• Custom evaluations: Teams can define their own quality criteria in natural language, such as whether the agent followed a required process, captured the right information, or handled a conversation correctly.

• Detailed call logs: Each call can include transcripts, metadata, status, timing, and other conversation details, which makes individual failures easier to investigate.

• Automated pre-launch testing: The Test Center can simulate customer conversations and evaluate them against measurable criteria before changes reach real callers.

• Production analytics: Dashboards help teams monitor call volume, call status, success rates, and agent performance without manually exporting every conversation first.

• Additional QA options for larger teams: Synthflow can be extended with third-party testing and monitoring tools for more advanced production monitoring, regression testing, latency analysis, and adversarial testing.

Limitations

• Advanced QA may require another tool: Teams that need deeper continuous testing, security testing, or more sophisticated production monitoring may end up pairing Synthflow with a dedicated testing/monitoring platform.

• Custom evaluations still need good QA criteria: The platform can automate scoring, but teams still need to clearly define what a successful or failed conversation looks like.

• More focused on AI-agent quality than human-agent coaching: Synthflow is better suited to evaluating AI conversations than running traditional supervisor-led QA and coaching programs.

• Best fit when calls already run through Synthflow: If your contact center operates mainly on another voice platform, using Synthflow purely as a monitoring layer is less compelling.

Pricing

Synthflow uses pay-as-you-go pricing, with larger deployments moving to Enterprise contracts based on usage and requirements. 

ElevenLabs

ElevenLabs is a good fit for teams that care about both how an AI agent sounds and how well it performs once real calls start coming in. Its ElevenAgents product now includes testing, evaluations, conversation analysis, production analytics, and real-time monitoring, so teams can move beyond simply listening to recordings and start measuring whether conversations are actually successful.

Key Strengths

• Custom success evaluations: Teams can define their own criteria for whether a conversation was successful, including process adherence, goal completion, and customer experience.

• Conversation analysis: ElevenLabs can analyze transcripts for sentiment, structured data, conversation outcomes, and other signals that help explain why a call went well or poorly.

• Automated testing: Teams can create repeatable tests for agent behavior and run the same scenario multiple times to spot inconsistent responses or edge cases.

• Production analytics: Dashboards can break performance down by agent, time period, language, call type, model, and other dimensions, which makes it easier to identify patterns across conversations.

• Real-time monitoring: Teams can monitor active conversations through live transcripts and agent events instead of waiting until the call has finished to investigate a problem.

Limitations

• QA is centered on ElevenAgents: The monitoring and evaluation features are most useful when the conversations themselves are already running through ElevenLabs.

• Not a traditional workforce QA platform: It is better suited to evaluating AI-agent behavior than managing supervisor scorecards, coaching programs, and large human-agent QA operations.

• Custom evaluations still need clear criteria: Teams have to decide what success, failure, or acceptable behavior looks like before automated scoring becomes meaningful.

• Voice quality is still a major part of the product: Teams looking only for a standalone QA layer may find they are adopting more conversational AI infrastructure than they actually need.

Pricing

ElevenLabs plans start at $6 per month, with higher tiers offering more included usage and concurrency. ElevenAgents also supports usage-based pricing as call volume increases.

Twilio

Twilio is a good fit for larger contact centers that want to monitor both the technical quality of calls and the quality of the conversations happening on top of them. Its monitoring stack combines Conversation Intelligence for analyzing what was said with Voice Insights and Conversation Relay Insights for understanding issues like latency, interruptions, silence, and reliability.

Key Strengths

• Conversation analysis: Twilio Conversation Intelligence can extract sentiment, summaries, intent, and custom signals from call transcripts, which gives QA teams more than a basic recording review.

• Custom quality checks: Teams can create their own language operators to look for things like script adherence, risk signals, customer intent, or other criteria that matter to their QA process.

• Real-time monitoring: Conversation Intelligence can run during a live interaction, allowing teams to identify risky behavior or quality problems before the conversation is over.

• Technical call-quality visibility: Voice and Conversation Relay Insights help teams investigate latency, interruptions, silence, connection failures, and other issues that can make an AI call feel poor even when the agent logic is correct.

• Useful for mixed AI + human environments: Twilio can analyze both AI and human conversations, which makes it more practical for contact centers where calls regularly move between the two.

Limitations

• Monitoring is spread across multiple products: Conversation Intelligence, Voice Insights, and Conversation Relay Insights handle different parts of QA, so the experience is less consolidated than a purpose-built evaluation platform.

• More technical to configure: Getting useful custom analysis usually means defining operators, rules, and data flows rather than simply switching on a standard QA scorecard.

• Pricing can be harder to model: Conversation analysis, telephony, transcription, and other Twilio services can be priced separately, so the total QA cost depends heavily on the setup.

• Some monitoring features have product-specific restrictions: For example, Twilio notes that Conversation Relay Insights is not a HIPAA Eligible Service, which matters for regulated workflows.

Pricing

Twilio uses pay-as-you-go pricing. Conversation Intelligence is usage-based, while voice, transcription, and other communication services are charged separately depending on the configuration.

LiveKit

LiveKit is a good fit for technical teams that want deep visibility into how an AI voice agent behaves at the session level. Its approach to quality monitoring is more engineering-focused than traditional contact-center QA, combining recordings, transcripts, traces, logs, latency metrics, and testing tools so teams can investigate both conversational failures and problems inside the underlying voice pipeline.

Key Strengths

• Detailed session observability: LiveKit Cloud brings transcripts, traces, logs, audio recordings, and session metrics into one timeline, which makes failed calls easier to investigate.

• Latency and performance debugging: Teams can inspect timing across individual turns and different parts of the AI pipeline, which is useful when poor call quality is caused by slow responses rather than the conversation logic itself.

• Built-in testing and evaluations: LiveKit supports behavioral tests and full conversation simulations so teams can validate agent changes and catch regressions before deployment.

• Custom QA workflows: Developers can collect recordings, transcripts, metrics, and other session data and send them into their own monitoring or evaluation systems.

• Strong debugging flexibility: Traces can be exported to OpenTelemetry-compatible tools, giving technical teams more freedom to build monitoring around their existing observability stack.

Limitations

• More technical than most QA platforms: LiveKit's monitoring tools are designed primarily for developers, so operations or QA teams may need engineering support to get the most value from them.

• Not a ready-made contact-center scorecard: Teams looking for simple supervisor dashboards, coaching workflows, or prebuilt QA templates will need to build more of that layer themselves.

• Some deeper testing is developer-led: LiveKit provides strong testing infrastructure, but defining custom assertions, judges, and simulations requires more setup than a typical no-code QA tool.

• Cloud observability has deployment requirements: LiveKit's built-in agent observability depends on LiveKit Cloud infrastructure, so fully self-hosted deployments do not get the same monitoring experience.

Pricing

LiveKit starts at $50 per month, with additional usage-based costs depending on agent sessions, inference, telephony, recordings, and other infrastructure used.

Choosing the Right AI Call Quality Monitoring Tool

The right platform depends on what you are actually trying to improve. Some teams need better visibility into why AI calls fail. Others care more about automated scoring, regression testing, technical debugging, or giving operations teams an easier way to review conversations at scale.

For teams that want monitoring close to the AI + telephony stack

Plivo is a strong option if you want call monitoring, transcripts, performance reporting, and debugging closer to the same environment where your AI agents and calls already run. That can make it easier to understand whether a problem came from the conversation itself, the workflow, or the underlying call setup.

For developer-led teams that want customizable evaluations

Vapi makes more sense when your team wants to define its own evaluation logic and testing workflows. It gives developers a lot of flexibility around simulations, structured analysis, call artifacts, and custom success criteria.

For teams that want built-in QA without too much setup

Retell AI is a good middle ground. Its post-call analysis, AI quality assurance, simulations, and monitoring features make it easier to evaluate production conversations without building the entire QA layer yourself.

For teams evaluating large volumes of AI calls

Bland AI is worth considering if automated scoring at scale is the priority. Its evaluation framework is useful for reviewing large batches of calls against custom criteria and finding patterns that would be difficult to catch manually.

For ops teams that want a more visual QA workflow

Synthflow AI is better suited to teams that want to create evaluations, review call logs, and run tests without making engineering responsible for every QA change. More advanced teams can also extend the monitoring setup through integrations.

For teams that care heavily about conversational quality

ElevenLabs is a good fit when call quality includes not just whether the workflow succeeded, but also whether the interaction felt natural. Its testing, conversation analysis, success evaluations, and production analytics make it particularly useful for customer-facing AI agents.

For enterprises that need both conversation and technical monitoring

Twilio stands out when teams want to understand both what happened in the conversation and what happened underneath it. Conversation Intelligence can analyze the interaction itself, while Twilio's voice monitoring tools give teams more visibility into technical issues such as latency and connection quality.

For engineering teams that need deep observability

LiveKit is the strongest fit when monitoring means inspecting traces, logs, transcripts, recordings, latency, and individual parts of the real-time voice pipeline. It gives developers a lot of control, but it is less suited to non-technical QA teams looking for ready-made scorecards.

The main thing is to match the platform to the kind of QA problem you are trying to solve. If you are starting fresh and want quality monitoring to stay close to the same AI and telephony environment rather than adding another standalone system, Plivo is one of the more balanced options to put on the shortlist.

Conclusion

AI call quality monitoring is most useful when it helps you do more than just listen back to recordings. The real value comes from spotting failed conversations, understanding why they happened, testing changes before they go live, and giving teams a clearer way to improve AI agent performance over time.

The right choice depends on how your team works. Vapi and LiveKit give technical teams more control, Retell and Bland are strong for structured evaluations, and Synthflow is easier for ops-led teams. But if you want monitoring to sit closer to the same environment handling your AI agents and telephony, Plivo is one of the more practical options to evaluate because it keeps more of the call, agent, and performance data in one place.

Want to improve AI call quality without adding another disconnected QA layer?

If your team is already looking at voice AI for customer conversations, Plivo is worth putting on the shortlist. You can run the calls, review how the agents perform, identify where conversations break down, and improve the experience without stitching together as many separate systems.

Explore Plivo's built-in call monitoring