Listener QM
Quality Monitoring AI agents: Rate your bots, then help them improve.
Listener QM It evaluates 100% of your AI agents’ conversations (voice, chat, email) against your agent evaluation grid, providing evidence for each criterion. It then sends actionable feedback to the bot and its knowledge base: articles to be corrected, guidelines, and test scenarios—all without switching providers.
Get an immediate response from AI—no form required. Would you prefer to speak with an expert? Schedule an appointment.
A trusted third party to rate your bots, not their publisher.
Your AI agents are handling an increasing share of your customer interactions. Who ensures that they provide accurate responses, follow your procedures, and handle transfers properly? Listener QM applies the same evaluation criteria, documentation, and reporting to AI conversations as it does to your human agents.
Independent of the bot's publisher
Your AI provider's dashboard evaluates its own performance. CrossCX provides an external evaluation.
- Your criteria, not the supplier's
- Your conversations, without any samples selected by the bot
- A report that you can use to challenge the publisher
A single framework for humans and AI
Same criteria, same metrics, same reporting: you can finally compare like with like.
- Bot vs. Advisors, Point by Point
- Version N versus Version N-1 after a prompt change
- All of this is included in your existing CRM Dataviz reports
Every grade is verified
Justifiable in committee, applicable in coaching, enforceable against the supplier.
- One conversation excerpt for each criterion
- Human review of a sample by our analysts
- AI vs. Analysts Agreement Rate Published During Each Campaign
What You're Really Measuring in an AI Agent.
The metrics from your AI platform are still useful. CrossCX supplements them with an independent assessment, using your quality framework and providing evidence for each rating across four categories.
Resolution
Was the customer's need truly addressed, and did the customer return for the same reason?
Reliability
Answers based on your sources, procedures followed, and scope maintained.
Experience
Smooth transfers, the right tone—using the same criteria as for your advisors.
Economy
The cost of a truly productive discussion, as presented to the executive committee.
From your bot logs to an action plan, in five steps.
You don't need to change anything about your AI agent. We monitor its conversations, evaluate them using your scoring rubric, and provide you with metrics you can stand behind.
Log In
We retrieve the bot's conversations—transcripts, audio, triggered actions, context, and version—via a connector, API, or simple JSON/CSV export. Voice, chat, and email, all in the same stream as your human conversations.
Compatible EditorsGrade using the standard rubric
The " Listener QM " framework you’re already using applies to 100% of the bot’s conversations, enhanced with AI-specific criteria: anchoring, procedures, actions taken, transfer, and scope.
Customizing gridsProve
For each criterion, the excerpt that justifies the score. Our analysts review a sample; the agreement rate is published, and any disagreements are used to recalibrate the scoring grid—not to disregard it.
Automated & Augmented Quality ManagementCompare like with like
The bot compared to your advisors on the same topics. Today’s version compared to yesterday’s. All of this in “ CRM Dataviz,” by topic, channel, and version.
Quality ReportingAct
Deviation alerts are triggered as soon as an indicator drops below the threshold. Escalation reasons and unanswered questions are reported to Cross-Mining to populate the knowledge base. Structured feedback is sent back to the bot and its knowledge base: articles, guidelines, and test scenarios.
Discover Cross-Mining
From the grade to the correction: feedback your bot can handle.
Evaluation alone isn't enough. Every discrepancy identified by Listener QM becomes structured feedback in a format that your AI agent's platform can process: it's the advisor's debriefing, adapted for the bot.
Feedback by Conversation
Rating, failure criteria, and evidence excerpts, sent conversation by conversation to the publisher’s feedback tool.
Knowledge Corrections
Unlinked answers and unanswered questions grouped by reason: articles to be created or edited, saved as drafts in your knowledge base.
Guidelines and Procedures
For each recurring issue, a guideline or rule has been drafted for the publisher's console, ready for your teams to adopt.
Test Scenarios
Failed conversations become test cases that are replayed with each new version of the bot to verify that the error does not recur.
Two destinations, depending on what your tools support
Go to the bot platform
Conversation-based feedback, instructions, and test cases sent to the publisher’s feedback, evaluation, or testing API (such as ElevenLabs, Vapi, or Salesforce Agentforce), or delivered to its console.
Go to the Knowledge Base
Articles to be created or edited are saved as drafts in the database accessed by the bot (Mayday, Smart Tribune, Zendesk Guide, Salesforce Knowledge, ServiceNow, Confluence…), and then published after approval.
By export
For a custom-built bot or a tool without an API: a file containing corrections and test cases (JSON or CSV), provided to your teams or the developer for each campaign.
Nothing is released without approval from your quality teams, just as with the AI-assisted debriefing of your advisors. The next version of the bot is then evaluated using the same rubric: the version-specific deviation alert indicates whether the correction was successful.
In and out: one connector, two directions.
As input, Listener QM retrieves your AI agents' conversations to review them. As output, it sends the approved responses back to where they're needed: to the bot's platform or directly to the knowledge base it references.
The bot's conversations
Retrieved via connector, API, or export, within the same workflow as your human conversations.
- Transcripts and audio, voice, chat, and email
- Triggered actions, reason, and bot version
- Same pay scale and same pay rates as for your advisors
Feedback: When It's Useful
Feedback approved by your quality teams is sent back to the systems that power the bot's responses.
- To the bot platform: conversational feedback, instructions, and test cases
- Toward the Knowledge Base: Articles to be created or edited, saved as drafts
- Via JSON or CSV export for tools without an API
Nothing is published without approval: submitted articles are saved as drafts in your database, and your teams review them before they go live. See compatible AI agent editors and knowledge bases.
Regardless of which platform you use for your AI agent.
We evaluate the bot; we don’t replace it. All you need to do is access its conversations—via a connector, an API, or an export file you upload—on the same channels as your agents. Once approved, the corrections are sent back to the knowledge base that the bot references.
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GDTrademarks mentioned for compatibility purposes are the property of their respective owners. CrossCX is independent of these publishers and receives no compensation from any of them.
Three ways to get started, depending on your level of experience.
A bot doesn't have seats. So we bill based on the volume of conversations analyzed, not per user—and you can start with a simple audit before committing to anything.
One-time audit
An independent assessment of your AI agent's performance, presented to the committee.
- 500 to 1,000 conversations rated using the standard rubric
- Compare your advisors based on the same criteria
- Top Discrepancies with Supporting Evidence
- Recommendations for the Bot Editor and Knowledge Base
Continuous monitoring
100% of AI conversations are continuously monitored, with alerts triggered as soon as a metric falls below the threshold.
- Connector or API for your AI agent platform
- Drift Alerts by Prompt or Model Version
- CRM Dataviz Tables by Design, Channel, and Version
- Knowledge base gaps reported by Cross-Mining
- Feedback to the bot and its knowledge base
Calibration & Support
Our quality analysts review, evaluate, and document—in addition to conducting audits or monitoring.
- Human proofreading of a sample and published agreement rate
- Workshops on developing the grid with your quality teams
- Human Oversight Documentation for Your GDPR , and AI Act Compliance Requirements
- Quarterly meeting with the bot's developer
Pricing is based on a quote, depending on the monthly volume of conversations and the channels used. Already a Listener QM customer? AI agent evaluation can be added to your contract without requiring a new platform.
Evaluating AI Agents: Your Questions.
What is an AI agent's " Quality Monitoring "?
This involves evaluating the conversations handled by your chatbots, voicebots, and AI agents using a quality rubric—just as you do for your customer service representatives—covering resolution, reference to the knowledge base, adherence to procedures, quality of transfer, tone, and security.
Should we switch to a different bot editor?
No. Listener QM evaluates the bot without replacing it: you can simply access its conversations via a connector, API, or export. See compatible platforms.
How does CrossCX send feedback to my AI agent?
Each discrepancy is converted into structured feedback (conversational feedback, instructions, test scenarios) and sent to the bot’s platform, and content corrections are saved as drafts in its knowledge base. Without an API, everything is delivered via export.
Which knowledge bases are compatible?
The main platforms used by customer service teams: Mayday, Smart Tribune, Easiware, Zendesk Guide, Salesforce Knowledge, ServiceNow, Microsoft Dynamics 365 and SharePoint, Confluence and Jira, Notion, Guru, Intercom, Freshdesk, HubSpot, Zoho Desk, Genesys, and NICE CXone Expert. For other platforms, corrections are delivered via export.
Are the corrections applied automatically?
No. The feedback is reviewed and approved by your quality teams before being forwarded, and the items are added as drafts to your database: you decide what changes are made to your bot.
How can you verify that a correction worked?
The next version of the bot is evaluated using the same criteria. A comparison of versions N and N-1, along with deviation alerts, shows whether the corrected criterion has improved, and the test scenarios verify that the error does not recur.
Can we compare the bot to our advisors?
Yes. The same grid applies to both populations, pattern by pattern, in your tables CRM Dataviz.
Try out the framework in your own conversations.
No mock-up, no demo dataset: Send us an export of your AI agent, and we'll provide you with an independent evaluation—complete with evidence—based on your actual use case.
- You are exporting 200 conversations from your bot (voice, chat, or email)
- We grade them using the standard rubric, including evidence excerpts
- You will receive the report, the comparison with your advisors, and the initial feedback to forward to the bot
Other Features Quality Monitoring
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