7 Contact Center QA Tools for Coaching Human and AI Agents in 2026

If you run a mixed team of humans and AI agents, you need contact center QA software that scores conversations against your own standards and turns those scores into coaching your team can use. That is the problem this guide addresses. It is written for QA managers, CX directors, and contact center operations leads comparing platforms before a purchase or switch in 2026, and it judges each option on four practical criteria: scorecard flexibility, conversation coverage, coaching output, and support for AI and human teams. What follows is a ranked list of seven platforms, each with a clear best fit to help you match your operational needs to the right tool.

Our top pick is Agent Insight by Aissist for mixed AI and human contact center operations because, according to Aissist, it evaluates both agent types against company-defined criteria and connects the results directly to AI improvement. It scores AI and human agents against the standards you define, then feeds those results back into the AI so quality measurement can inform future performance. For sales-focused teams that need coaching tied to specific call moments and practice scenarios, Insight7 is a focused alternative. For BPOs and multichannel support organizations managing chat, email, tickets, and calls, CSAT.AI is another option worth considering.

You will see all seven options below in ranked order. Each was assessed against the same four criteria, with relevant trade-offs noted, so you can build a shortlist based on operational fit rather than brand size or familiarity.

How we chose

We did not rank the platforms on brand awareness. We compared the available public product information for each one against the four areas that matter most when you have to coach people and bots within the same operation.

Scorecard flexibility

Can you score agents against your own standards, or are you restricted to a vendor template? We favored tools that let teams define role-level criteria for empathy, resolution, policy adherence, and the other behaviors they actually monitor and coach.

Conversation coverage

Do you get automated scoring across all supported conversations, or only a limited sample for manual review? Broader coverage matters because coaching based on a handful of calls can miss recurring patterns and may feel inconsistent or unfair to agents.

Coaching output

Does the tool give you a number, or does it explain what to do next? We looked for specific recommendations, relevant call moments to review, and practical coaching steps rather than dashboards that leave managers to interpret every score themselves.

AI and human team support

Can the platform evaluate AI agents as well as people? Many contact centers now operate with a mix of human and automated agents, so a QA tool that only understands human calls can leave part of the operation’s quality risk unmeasured.

At a glance: what each tool is best for

The 7 best contact center QA software platforms for coaching human and AI agents

These seven platforms suit different use cases, ranging from hybrid AI and human operations to multilingual support teams working across regions. Use the best-for labels to identify a likely match, starting with our top recommendation at number one for teams that need to coach people and AI against a consistent but role-specific standard.

#1. Agent Insight by Aissist – Best for mixed AI and human contact center teams

*For hybrid operations that want QA tied to their own role level standards, with results that improve human coaching and AI agent performance.*

According to Aissist, Agent Insight Aissist evaluates both AI and human agents against criteria you define for each role rather than scoring everyone through the same generic vendor template. That distinction matters when your AI handles routine chats while human agents manage escalations, since each role can be judged on the behaviors that define good work for that particular job. Aissist positions the product for hybrid contact centers where QA needs to cover both workforces in one system without assuming that people and bots should follow an identical scorecard.

You also get more than a score, based on Aissist’s description of the platform. The company says Agent Insight returns specific improvement recommendations so team leaders know what to coach and where performance is falling short, rather than simply seeing who ranked low. Aissist also says the results feed into Evolve, its optimization layer, allowing the AI to learn from the highest-scoring agents under the organization’s own standards. In practice, that creates a connection between measuring quality and improving the AI’s future responses, an area that many conventional human-agent QA tools do not address directly.

There are trade-offs to weigh before choosing it. As with any custom QA rollout, teams should test their scorecards against representative human and AI conversations during a demonstration. The Evolve feedback loop is most useful if you already use Aissist AI agents, while teams running only human agents will receive less value from that particular capability. Check that its integration options suit your existing CCaaS and WFM systems before rollout.

Pros

• Evaluates AI and human agents using the same system and custom criteria
• Delivers recommendations about what to improve rather than only rankings
• QA output feeds AI improvement through Evolve, according to Aissist
• Role-level standards can reflect company policies instead of a fixed template
• Designed around the needs of hybrid contact center teams

Cons

• Custom role-level scorecards require careful configuration and calibration
• Evolve adds the most value for teams already using Aissist AI agents
• Integration requirements should be checked against the existing contact center setup

Who it’s best for: QA and CX leaders running mixed AI and human teams who want custom standards and a direct connection between QA findings and AI improvement.

#2. Insight7 – Best for sales focused contact centers tying QA to revenue coaching

*For revenue teams that want every call scored and every score turned into practice.*

Insight7 says it offers full call coverage through custom scorecards. You can bring in an existing scorecard or build one in the platform, which helps when your sales process changes frequently and you do not want every adjustment to depend on professional services. Coverage includes sales calls, support tickets, and other interaction types, allowing revenue and service conversations to sit within the same quality system rather than being reviewed through separate processes.

Its main distinction is the way coaching is tied to each score. A score sitting in a dashboard does not change behavior by itself, so Insight7 turns evaluations into targeted coaching plans that include call moments to review and practice scenarios intended to close a specific performance gap. If you manage AEs, SDRs, or account managers who spend much of their working day on calls, that moment-level detail can reduce the time coaches spend searching through recordings before they can give useful feedback.

The trade-off is its focus. The depth of sales coaching may be more than a small or entirely support-focused team needs, and buyers should confirm fit when multilingual support or bot evaluation is the priority. A sales conversation can help teams clarify implementation requirements and total cost.

That emphasis on learning from actual conversations reflects wider interest in training systems built around mistakes and specific behaviors, including experiments such as BCG training an AI agent on the worst sales habits to demonstrate what effective selling should avoid.

Pros

• Full call coverage instead of relying only on small manual samples
• Custom scorecard import or a relatively quick build from scratch
• Coaching plans include relevant moments and practice scenarios
• Covers sales and support interaction types in one system

Cons

• Custom scorecards need maintaining as sales processes change
• Coaching depth may be excessive for very small, support-only teams

Who it’s best for: Sales-led contact centers where QA needs to improve revenue behavior and call execution rather than serve only as a compliance check.

#3. MiaRec – Best for teams wanting survey free CX metrics alongside automated QA

*For mid market teams that need CX signal without chasing survey responses.*

MiaRec is a conversational intelligence and automated quality management solution designed to improve customer service without requiring complex analytics tools or a dedicated data team. It combines automated QA with survey-free CX metrics such as CSAT and NPS, according to the company’s product information, which means teams can monitor signals related to sentiment and satisfaction when customers do not complete surveys. For many support leaders, that provides another source of customer experience data alongside conventional response-based programs.

Customization comes through its Prompt Designer, which is intended to provide teams with visibility and control as they adjust evaluation criteria. You can adapt prompts and criteria when policies change, while the vendor positions the platform as a way to improve customer experience, reduce churn, and identify possible revenue opportunities. If you do not have analysts on staff, the emphasis on managing the platform without a dedicated data team may be a practical advantage during day-to-day use.

Some onboarding effort is still likely. Prompt-based customization provides flexibility, but nontechnical QA managers may need time to learn how to phrase, test, and refine prompts so scoring remains useful and consistent.

Pros

• Survey-free CSAT and NPS can reduce dependence on customer response rates
• Automated QA supports greater coverage without adding manual reviewers
• Prompt Designer allows teams to tailor evaluation criteria
• Positioned around CX improvement, churn reduction, and revenue opportunities

Cons

• Survey-free CX indicators should be considered alongside direct customer feedback
• Prompt design may require onboarding and testing for nontechnical users

Who it’s best for: Support and CX teams that want automated QA alongside usable CX metrics without operating separate survey programs or building a dedicated analytics function.

#4. Konnect Insights – Best for multilingual and global support teams

*For global operations that need every interaction scored in every language.*

Traditional QA can be slow, manual, and incomplete, particularly when contact center operations span several regions. Konnect Insights addresses this with AI scoring of interactions against criteria defined by the customer, including empathy, resolution quality, policy adherence, and response speed. Assessors can review and refine the results rather than beginning every evaluation from scratch, allowing QA specialists to spend more of their time on judgment, exceptions, and calibration instead of routine volume processing.

The clearest differentiator is its language coverage. According to the vendor, the platform automatically scores interactions across supported languages, which is relevant if you run offshore sites, follow-the-sun support, or serve customers in multiple markets. Teams can define what good performance looks like and apply those expectations consistently, rather than relying on entirely separate review processes for each region or language.

The model still assumes a meaningful level of human oversight. You will need QA resources for review, refinement, and calibration, so teams should not treat the platform as a set-and-forget system.

Pros

• Multilingual automated scoring across supported interactions
• Custom criteria can cover empathy, resolution, policy, and speed
• Reviewer workflow focuses specialists on judgment and calibration
• Reduces the volume burden associated with manual sampling

Cons

• Still requires human QA time for refinement and calibration
• Language-by-language QA expectations still require careful calibration

Who it’s best for: Multilingual or global support teams that need consistent automated scoring across languages without increasing QA headcount in direct proportion to conversation volume.

#5. Oversai – Best for teams connecting QA results to sentiment and business outcomes

*For leaders who need to prioritize quality work by customer and business impact.*

Oversai treats a QA scorecard as an ongoing quality system rather than a periodic audit. A useful AI scorecard needs to do more than assign a number, and Oversai applies the same criteria consistently while pointing reviewers to the conversation evidence behind each result, flagging exceptions, and supporting a feedback loop for calibration. That evidence trail can help coaches understand why a score was assigned and make it easier for agents to see the basis for specific feedback.

Its main point of difference is prioritization. Oversai connects QA results to customer sentiment and operational outcomes so teams can focus their attention on the issues with the greatest apparent impact. Rather than treating every low score as equally urgent, reviewers can begin with interactions associated with churn risk, negative sentiment, or repeat contacts. For QA leaders who need to explain investments in headcount and tooling through business outcomes, that connection can make the results easier to communicate internally.

You may need to connect existing CX data to receive the full benefit of outcome linking, so integration effort should be part of the buying conversation.

Pros

• Links QA scores with sentiment and operational outcomes for prioritization
• Shows conversation evidence behind scores to improve transparency
• Exception flagging helps reviewers find outlier interactions
• A built-in calibration feedback loop supports more consistent scoring

Cons

• Outcome linking may require integration with existing CX data sources
• Impact-based prioritisation depends on the quality of the underlying CX data

Who it’s best for: QA and operations teams that need to connect quality work with CSAT, churn, and efficiency outcomes instead of looking only at individual agent metrics.

#6. OnScript AI – Best for fast moving teams needing rapid turnaround with compliance certifications

*For regulated or fast growth teams that need transcripts and scores in minutes.*

OnScript AI helps contact centers automate quality assurance through AI-based conversation analysis. According to the vendor, the platform analyzes calls and delivers transcripts, sentiment, intent, and scoring within minutes. This reduces reliance on manual reviews and limited sampling, giving teams a faster and more consistent QA process as call volumes grow. If coaches regularly face a review backlog, turnaround time is likely to be one of the most relevant parts of the product proposition.

Compliance is another part of the vendor’s pitch. OnScript lists SOC 1, SOC 2, and ISO 27001 certifications, although regulated buyers should verify the scope, current status, and relevance of any certification during security review. This information may matter to teams in healthcare, financial services, insurance, and other regulated sectors where security and procurement checks can delay implementation. The overall approach is automation-first and intended to reduce process overhead.

Buyers should still assess scale and functional depth carefully. Enterprise teams should examine support capacity, coverage hours, implementation resources, and roadmap alignment as part of their diligence rather than relying only on the speed of automated analysis. If formal coaching plans are essential, test scorecard and coaching workflows in a pilot.

Pros

• Broad call coverage with transcripts, sentiment, intent, and scoring delivered quickly
• Vendor-listed certifications may support security reviews for regulated buyers
• Automation-first design can reduce manual review bottlenecks
• Consistent scoring can help teams manage growing call volume

Cons

• Buyers should assess support capacity and scaling requirements
• Teams needing formal coaching plans should test the workflow before rollout

Who it’s best for: Fast-moving or regulated teams that need quick transcript and scoring turnaround alongside documented security information they can verify during procurement.

#7. CSAT.AI – Best for BPOs and high growth support orgs managing multichannel work

*For outsourced and scaling teams that live in chat, email, tickets, and calls at once.*

CSAT.AI says it automatically analyzes customer interactions across chat, email, tickets, and calls, scoring them for quality, brand tone, empathy, effort, and customer sentiment. That omnichannel coverage is a central reason BPOs and growing support organizations may shortlist it. Teams can apply consistent standards across different client programs or product lines without operating a separate quality tool for every channel.

The scoring dimensions also extend beyond those offered by call-only tools. Brand tone and customer effort sit alongside quality, empathy, and sentiment, which is useful when agents move between asynchronous tickets and live conversations throughout the day. The vendor says the platform is built for CX teams, BPOs, and fast-growing support organizations, placing multiteam reporting and channel coverage at the center of its positioning.

Breadth brings a trade-off. Covering four channels in one platform may provide less depth within an individual channel than a specialist product, so voice-heavy teams should compare call-level features and coaching workflows closely.

Pros

• Omnichannel analysis across chat, email, tickets, and calls
• Scoring covers tone, empathy, effort, quality, and sentiment
• Positioned for BPO operating models and growing support teams
• Broad interaction coverage across several service channels

Cons

• Channel breadth may offer less call-specific depth than specialist tools
• Consistent standards across channels still require team-level calibration

Who it’s best for: BPOs and scaling support organizations that need a single QA view across multiple channels, teams, and client programs.

Frequently asked questions

What is contact center QA software and why does it matter in 2026?

It is the system used to score conversations, identify coaching needs, and track whether service quality is improving over time. In 2026, it matters more because many operations run human and AI agents side by side, making limited spreadsheet-based reviews less representative of the work customers actually experience. A modern platform can score supported interactions against your standards and direct coaches to the moments that need attention.

How is AI assisted QA different from manual QA?

Manual QA generally means reviewers score a limited selection of interactions, which can be slow and may appear inconsistent to agents if the sample does not reflect their normal work. AI-assisted QA can score a much broader set of conversations automatically, then direct human reviewers toward exceptions, disputed results, and edge cases that require judgment. People still play an important role, but their time shifts toward calibration, fairness, and coaching rather than searching through calls at random.

Should you aim for 100 percent coverage or stick with sampling?

If quality coaching is a core part of your operation, broader coverage is usually the better goal. Sampling can hide recurring issues and make it difficult to judge whether coaching has changed behavior across an agent’s overall workload. Full coverage can reveal patterns by agent, team, intent, or sentiment, allowing reviewers to prioritize conversations with greater customer impact. Sampling may still suit very small teams with low interaction volumes, but growing operations often need wider visibility as volumes and channels increase.

Which is better: custom scorecards or vendor templates?

Custom scorecards are usually a better fit when your organization has distinct roles, policies, service expectations, or brand-tone requirements because they measure the behaviors you actually coach. Vendor templates can help a team get started quickly, but generic criteria may emphasize politeness while overlooking the resolution and policy standards that matter internally. Look for tools that let you import or build a scorecard efficiently, test the resulting evaluations, and refine the criteria as policies and customer needs change.

Which tools can evaluate AI agents as well as human agents?

In this guide, Agent Insight by Aissist is the option that explicitly states it evaluates AI and human agents against company-defined criteria. If hybrid coverage is required, ask each vendor how it ingests bot transcripts, applies role-specific criteria, manages calibration, and uses QA findings to improve automated responses.

What is the difference between a QA score and a coaching recommendation?

A score indicates where performance was weak, while a coaching recommendation explains what to correct and how the agent can work on it. Useful recommendations identify the behavior involved, connect it with the relevant conversation moment, and suggest a practice scenario or suitable next action. That movement from measurement toward practical feedback is one reason teams are adopting AI-assisted coaching, reflected in reporting on how AI sales coaches are taking over training by making feedback more specific and timely.

Which tool fits your team best

If you run mixed AI and human operations and want QA tied to your own standards, with a feedback loop into AI improvement, start with Agent Insight by Aissist. If you lead a sales-heavy operation where the path from scoring to practice matters most, Insight7 may fit your workflow. Teams that need survey-free CSAT and NPS without a dedicated data function can look at MiaRec, while global teams that need consistent scoring across languages should shortlist Konnect Insights. Oversai suits organizations that want to prioritize quality work through sentiment and business impact, and regulated or fast-moving teams seeking rapid transcripts and scores can evaluate OnScript AI while verifying its security documentation. BPOs and growing support organizations managing chat, email, tickets, and calls in one place should consider CSAT.AI. The right contact center QA software depends on your coverage requirements and coaching process, so shortlist the closest fits, run a pilot using real conversations, and choose the platform your coaches can work with consistently.