The best AI tools to monitor and grade support tickets score 100% of conversations for correctness against policy, not only tone, and grade both AI and human agents. Lorikeet's Coach does this with proactive recall.

Most quality assurance programs review between 1% and 3% of support tickets. A reviewer pulls a small sample each week, scores it against a rubric, and assumes the unread 97% looks roughly the same. For decades that was the only economically viable way to run QA, because a human can only read so many conversations in a day.

That assumption breaks down fast. The failures that hurt a support operation the most, a wrong refund amount, a policy exception granted to the wrong customer, a compliance step skipped, are rare by definition. A 2% sample is statistically unlikely to surface a problem that happens in 1 of every 500 tickets, and when it does surface one, you have no way to know how many more slipped past unread. The tickets you never read are the tickets that get you in trouble.

AI changes the economics. A model that reads and scores a conversation costs a fraction of a cent and runs in seconds, so reviewing 100% of tickets is now cheaper than the spreadsheets and reviewer hours most teams already spend on their 2% sample. The better tools go further than coverage: they grade whether a resolution was actually correct against your policies, not only whether the agent sounded polite, and they score AI agents and human agents on the same framework. This guide ranks 8 tools that do some version of that, with notes on where each one is strong and where it falls short.

What to look for in an AI QA tool

The phrase "AI QA" covers a wide range of products, from tone classifiers bolted onto a helpdesk to systems that read your standard operating procedures and judge correctness. Before comparing vendors, it helps to be clear on the four things that separate a real ticket-grading tool from a sentiment dashboard.

• Coverage. Does it score every ticket, or does it sample? Sampling-based tools inherit the same blind spot as manual QA: rare, high-cost failures stay invisible. 100% coverage is the single biggest reason to adopt AI for QA in the first place.

• Correctness versus tone. Tone and sentiment scoring is easy and most tools do it. The harder and more valuable capability is grading correctness: did the agent follow the policy, apply the right exception, take the right action, and resolve the underlying issue? That requires the tool to read your SOPs and reason against them.

• AI agents and human agents on one framework. Many teams now run a mix of AI resolution and human agents. A QA tool that only grades humans, or only grades its own AI, leaves half the operation unmeasured. Scoring both on the same rubric is what makes the numbers comparable.

• Proactive recall. A score on every ticket is useful. A tool that actively flags the tickets it is least confident about, and surfaces patterns across thousands of conversations a human would never connect, turns QA from a reporting function into an early-warning system.

Quick comparison

How these tools were selected

This list focuses on tools that monitor and grade support ticket resolutions, rather than general analytics or routing products. The selection criteria were:

• It scores resolutions, not only metadata. The tool has to read the conversation and form a judgment about it, not only report handle time and CSAT.

• It is usable in production support, today. No research previews or roadmap promises.

• It targets quality, not deflection. The job is grading whether tickets were resolved well, separate from whether the AI deflected them.

The evaluation factors that shaped the ranking were coverage (sampling versus 100%), depth of grading (tone versus correctness against policy), whether the tool grades both AI and human agents, the presence of proactive recall, and how honestly each vendor documents its limits.

What is AI support QA, or auto QA?

AI support QA is the use of a language model to review support conversations and score them against a quality rubric, automatically and at scale. Traditional QA depends on human reviewers reading a small sample; auto QA reads every ticket. The term "auto QA" usually implies that scoring happens without a human pulling each conversation, and that the system can grade the substance of a resolution, not only surface signals.

A capable AI QA tool typically does the following:

• Reads the full conversation, including any actions the agent took and the systems it touched.

• Compares what happened against the team's policies, SOPs, or scorecard.

• Produces a per-ticket score with a rationale a reviewer can audit.

• Flags tickets that look wrong or that the model is unsure about.

• Aggregates scores into trends so managers can see where quality is slipping.

The 8 best AI tools to monitor and grade support ticket quality

1. Lorikeet (Coach / Auto QA)

Best for: Teams that need to grade correctness against policy across 100% of tickets, on both AI and human agents, in regulated or high-stakes industries.

Lorikeet is an agentic AI support platform whose QA layer, called Coach, runs an Auto QA capability that produces a Ticket Quality Score (TQS) for every conversation. The distinguishing design choice is that Coach is built to grade correctness, not only tone. It reads the team's SOPs and policies and judges whether the resolution followed them: was the right exception applied, was the refund amount correct, was the required step taken, was the customer's actual problem solved. That is a harder problem than sentiment classification, and it is the problem most QA programs actually care about.

Coverage is the second differentiator. Rather than sampling, Auto QA scores 100% of tickets, AI-handled and human-handled, on the same framework. That matters for teams running a mix of automation and human agents, because it makes the two directly comparable and removes the blind spot that sampling-based QA inherits from manual review. In internal benchmarking the system catches roughly 99.7% of bad tickets, and it adds proactive recall: instead of waiting for a manager to ask, Coach surfaces the tickets it is least confident about and flags patterns across the queue that a human reviewer would have no realistic way to connect.

That last capability has shown up clearly in deployments. At a QA design-partner company, 689 scored tickets returned a 93% Ticket Quality Score, and a repetition check caught a class of failures that were invisible inside a queue of more than 6,500 tickets, the kind of slow, repeating error a 2% sample would almost never catch. A separate company replaced its manual spreadsheet QA with 100% automated coverage and now scores roughly 400 fee reversals per week, work that previously depended on a reviewer reading a fraction of cases by hand.

Lorikeet is honest about the boundaries. Auto QA still has false negatives that are being tuned, so it is positioned as a system that dramatically widens coverage and catches the overwhelming majority of problems, not as an infallible judge. Clinical and medical topics have a hard ceiling and always require human oversight, by design. And Lorikeet does not run a proprietary house model; it orchestrates third-party and open-weight models with automatic failover. The pitch is coverage and correctness you can audit, with clearly stated limits, rather than a black box that claims perfection.

Key features:

• Ticket Quality Score on 100% of tickets, AI and human, on one framework

• Grades correctness against your SOPs and policies, not only tone or sentiment

• Roughly 99.7% bad-ticket catch rate in internal benchmarking

• Proactive recall: flags low-confidence tickets and cross-queue failure patterns

• Per-ticket rationale and a per-conversation audit trail reviewers can inspect

• Pairs with Lorikeet's resolution agent, so QA and resolution share one system

• SOC 2 Type II, ISO 27001, HIPAA, and GDPR coverage

Pricing: Custom, outcome-based. Lorikeet's resolution platform starts around $60K rather than the high six-figure floors common in enterprise CX.

2. Zendesk QA (formerly Klaus)

Best for: Teams already on Zendesk that want AI-assisted QA on human-agent conversations.

Zendesk QA, the product that grew out of the Klaus acquisition, is one of the more mature AI QA tools for human agents. It can score conversations automatically and apply AI to surface tickets worth reviewing, push beyond the traditional small sample toward full coverage, and run scorecards and calibration sessions across a team. For organizations standardized on Zendesk it integrates tightly and is a natural extension of existing workflows.

Its grading leans toward CSAT prediction, sentiment, and scorecard adherence rather than deep correctness reasoning against a complex policy set, and its center of gravity is human-agent QA inside the Zendesk ecosystem rather than grading an external AI resolution agent. Teams running AI resolution on a different platform will find the fit looser.

Key features:

• AI-assisted conversation scoring and review-worthy ticket detection

• Customizable scorecards, calibration, and reviewer workflows

• Sentiment and CSAT-prediction signals

• Native to the Zendesk ecosystem

Pricing: Add-on to Zendesk Suite, priced per agent. G2 ratings are strong on the underlying Klaus product.

3. Decagon (Watchtower)

Best for: Teams running Decagon's AI agent who want monitoring of its behavior.

Decagon's Watchtower is the monitoring and analytics layer for Decagon's own AI support agent. It gives teams visibility into how the agent is handling conversations, where it is escalating, and how performance is trending, which is useful operational telemetry for a Decagon deployment. As a monitoring surface for the agent it sits closest to, it does its job.

The scope is the constraint. Watchtower is built around Decagon-handled tickets rather than acting as an independent grader across an entire mixed operation, and Decagon is not HIPAA compliant, which has been a deciding factor against it in healthcare evaluations. Teams that need policy-correctness grading across both AI and human agents, or who operate under HIPAA, should weigh that carefully. For a deeper side-by-side, see our Lorikeet vs Decagon comparison.

Key features:

• Monitoring and analytics for Decagon's AI agent

• Escalation and performance trend visibility

• Tight coupling with Decagon's resolution layer

Pricing: Bundled with Decagon's platform; $50K+ annual platform fee plus per-conversation or per-resolution usage.

4. Intercom Fin (CX Score)

Best for: Intercom-native teams that want conversation scoring alongside Fin's resolution.

Intercom's CX Score grades conversations inside Intercom, covering both Fin AI resolutions and human-agent conversations. Because it lives in the same platform that handles the tickets, setup is quick and the data is immediately at hand, and Fin itself is a strong resolution product with broad channel coverage. For teams committed to Intercom it is a sensible, low-friction way to get automated scoring.

The trade-offs are platform-bound. Scoring is most useful inside Intercom, the depth of correctness reasoning against bespoke policy is more limited than purpose-built grading, and Intercom's data-retention model can be a problem for industries with multi-year compliance retention requirements.

Key features:

• CX Score across Fin AI and human conversations

• Native to Intercom, fast to turn on

• Broad channel coverage through Fin

Pricing: Tied to Intercom and Fin; Fin is published at $0.99 per resolution.

5. Salesforce

Best for: Enterprises standardized on Service Cloud that want QA inside their existing CRM.

Salesforce offers QA and conversation-review capabilities within Service Cloud, and with its Agentforce and testing tooling it can apply rules and AI to flag and review cases. For large organizations already running on Salesforce, keeping QA in the same platform as the rest of the support operation has obvious data-gravity and reporting advantages.

The cost is complexity. Getting meaningful AI scoring usually involves Data Cloud and configuration work, deployments are heavier, and the correctness grading is rules- and sampling-oriented rather than reading free-form policy and reasoning about it the way a purpose-built grader does. It is powerful for teams with the Salesforce investment to support it, and overkill for teams without one.

Key features:

• QA and review inside Service Cloud

• Agentforce and testing tooling for AI agents

• Deep integration with the broader Salesforce platform

Pricing: Roughly $2 per conversation or Flex Credits at about $0.10 per action, typically requiring Data Cloud.

6. Forethought

Best for: Teams using Forethought's AI triage and agent-assist who want QA on that workflow.

Forethought builds AI triage, agent assist, and resolution tooling, with monitoring and analytics over the conversations its AI touches. It is genuinely useful for teams that have adopted Forethought's automation and want visibility into how it is performing. Following its acquisition by Zendesk in early 2026, its longer-term roadmap is converging with the Zendesk QA stack.

As with the other agent-bundled monitors, its QA is scoped to Forethought-handled conversations rather than acting as an independent grader across a whole mixed AI-and-human operation, and the acquisition introduces some roadmap uncertainty as the products integrate.

Key features:

• Monitoring and analytics over Forethought's AI workflows

• Triage and agent-assist quality signals

• Converging with Zendesk's QA tooling post-acquisition

Pricing: Custom; now sold within the Zendesk portfolio.

7. Cognigy

Best for: Contact centers with heavy voice and IVR volume that need conversation analytics.

Cognigy, part of NiCE, is strong in voice and IVR automation, and its analytics give contact centers visibility into how automated conversations are flowing across high-volume voice channels and many languages. For voice-first operations that is a real strength, and few of the AI-native QA tools handle voice as seriously.

Its analytics are oriented toward conversation flow and containment in the contact-center sense rather than grading the correctness of a resolution against detailed support policy, and it is a contact-center overlay rather than a native helpdesk, so it is a better fit for telephony-led operations than for text-first support teams focused on ticket-quality grading.

Key features:

• Voice and IVR conversation analytics

• Strong multilingual coverage

• SOC 2, ISO 27001, GDPR, with on-prem and private-cloud options

Pricing: Custom, typically enterprise-scale.

8. Ada

Best for: Teams running Ada's AI agent who want monitoring of its resolutions.

Ada is a well-regarded AI resolution platform with monitoring and reporting over the conversations its agent handles, giving teams insight into resolution rates and where the agent is struggling. As a way to keep an eye on an Ada deployment it works, and Ada carries solid certifications including SOC 2 and HIPAA with zero data retention.

The QA scope is again the limit. Monitoring centers on Ada-handled tickets rather than independently grading both AI and human agents across the whole operation, and its scoring is closer to resolution analytics than deep correctness grading against bespoke policy. Teams wanting an independent grader spanning their entire support surface will find it narrow.

Key features:

• Monitoring and reporting over Ada AI resolutions

• Resolution-rate and escalation analytics

• SOC 2, HIPAA, GDPR, with zero data retention

Pricing: Custom usage-based, generally per-resolution.

How to choose an AI QA tool

Start with coverage. If a tool samples, you are buying the same blind spot manual QA already has. The whole reason to apply AI here is that scoring every ticket is now cheap, so a tool that still samples is leaving its main advantage on the table. Ask directly what percentage of tickets get scored, and treat anything short of 100% as a meaningful limitation for catching rare failures.

Decide whether you need correctness or just tone. Sentiment and CSAT prediction are table stakes and almost every tool offers them. If your failures look like wrong refund amounts, missed policy steps, or incorrect exceptions, you need a tool that reads your SOPs and grades the substance of the resolution. Many tools that market themselves as QA only score how the conversation felt, which tells you nothing about whether it was right. For a fuller treatment of what QA actually means here, see what QA means in customer service.

Check whether it grades AI and human agents on one framework. Most support teams now run a blend of automated resolution and human agents. If your QA tool only grades one side, your reported quality number describes half the operation. Scoring both on the same rubric is what makes the comparison honest and the trend lines meaningful. We go deeper on this in our guide to QA and coaching tools for human agents.

Weigh integration against independence. Agent-bundled monitors (Decagon, Ada, Forethought, Intercom) are easy to turn on but tend to grade only the tickets their own agent handled. A more independent grader covers your whole support surface regardless of who or what resolved each ticket. Decide whether you want telemetry for one agent or QA for the entire operation.

Confirm compliance fit. In fintech, healthtech, and insurance, certifications and data-retention terms can rule a tool out before features matter. Decagon's lack of HIPAA compliance and short retention windows on some platforms are common disqualifiers in regulated evaluations. Confirm SOC 2, HIPAA where relevant, and retention terms early. Background reading on automated QA for customer support and the best AI QA tools for support can help frame the trade-offs.

Feature matrix

Two honest caveats on the Lorikeet column. Auto QA still produces some false negatives that are actively being tuned, so the roughly 99.7% catch rate is a benchmark figure, not a guarantee that nothing slips through. And clinical or medical topics carry a hard ceiling that always requires human oversight, rather than being fully automated. Lorikeet also does not run a proprietary model; it orchestrates third-party and open-weight models with failover. The advantage is coverage and policy-correctness you can audit, with the limits stated plainly.

Why Lorikeet wins on ticket-quality grading

The recurring failure of support QA is not that teams lack rubrics. It is that they only ever read a sliver of their tickets, so the rare, expensive mistakes stay hidden until a customer or a regulator finds them first. Lorikeet's Coach attacks that directly by scoring 100% of tickets, AI and human, against the team's actual policies rather than against a tone checklist.

The proof is in what the coverage surfaces. At a QA design-partner company, 689 scored tickets returned a 93% Ticket Quality Score, and a repetition check caught a class of failures that were invisible inside a queue of more than 6,500 tickets, exactly the slow, repeating error a sampling approach is built to miss. Another company retired its manual spreadsheet QA in favor of 100% automated coverage and now scores around 400 fee reversals per week without a reviewer reading each one by hand. In internal benchmarking, Coach catches roughly 99.7% of bad tickets and proactively recalls the ones it is least sure about.

None of that is pitched as magic. Auto QA still has false negatives under active tuning, clinical topics keep a human in the loop by design, and the models underneath are orchestrated third-party and open-weight systems rather than a house model. What Lorikeet offers is the combination that the agent-bundled monitors and the tone-focused tools each miss: full coverage, correctness graded against policy, AI and human agents on one framework, and an audit trail you can actually inspect. For teams in fintech, healthtech, and insurance where a missed failure is a compliance event, that combination is the differentiator.

If you want to see Auto QA and the Ticket Quality Score on your own tickets, book a demo.