AI to evaluate conversations based on custom criterias
under review
N
Nabilah Binti Salleh
Business Problem:
- Managers currently have no scalable way to assess conversation quality across AI Agent and human agent interactions — they're manually reading through closed conversations to spot problems.
- As AI Agent volume grows, this doesn't scale. The only in-platform option today is the CSAT workflow template, which captures customer satisfaction but doesn't tell managers what went wrong (e.g. was the issue actually resolved, was the handover smooth, did the agent follow the right process).
- The alternative is stitching together an n8n + AI workaround, which requires developer setup and isn't accessible to most teams.
- A single sentiment score (positive/negative/neutral) on its own also wouldn't be enough for managers who need to evaluate conversations against criteria specific to their own process.
Desired Outcome:
Let admins/managers define their own scoring criteria (beyond just sentiment) that AI applies automatically to every closed conversation — for example, issue resolved, handover smoothness, process adherence, tone appropriateness. Scores would surface in reporting so managers can filter, flag, and drill into conversations that fall below a threshold, without reading every transcript manually.
S
Shi Hui
Merged in a post:
AI: Analyze and QA Closed Conversations
Attia Saleh
Business Problem
Currently, the AI Agent cannot process or analyze conversations once they are closed. This prevents businesses from using AI to perform post-chat quality assurance. As a result, there’s no scalable way to validate whether AI Agents are following internal standards, selecting the correct closing reasons, or writing accurate closing summaries.
Desired Outcome
To have AI to read and analyze closed conversations. This would enable teams to:
- Verify if agents selected the correct closing reason
- Check for accurate and meaningful closing summaries
- Evaluate agent performance and conversation handling quality
- Build internal QA metrics such as IQS
- Identify training opportunities and ensure compliance with internal guidelines
S
Shi Hui
updated the status to
under review
A
Alyaa See
Merged in a post:
AI Quality Agent to Monitor Customer Conversations and Detect Poor Service
Carlos Portilla
We would like to request an AI Quality Agent that can automatically monitor and analyze all customer conversations across channels such as WhatsApp, chat, email, CRM, or other integrated messaging tools.
The main goal is to identify conversations where the customer may have been upset, poorly assisted, left without a clear answer, or where the conversation was closed without proper resolution.
This agent should help supervisors detect service issues before they become formal complaints, reduce manual review time, and improve the overall customer experience.
The agent should be able to:
Review all customer-advisor conversations automatically.
Detect upset, frustrated, confused, or dissatisfied customers.
Identify conversations with poor or incomplete service.
Flag cases where the advisor did not provide a clear solution.
Detect weak conversation closures where there was no confirmation of resolution or customer satisfaction.
Identify conversations where the customer stopped responding after a negative experience.
Highlight possible lost sales or missed business opportunities.
Detect cases that should have been escalated to a supervisor or coordinator.
Generate alerts for high-risk or critical conversations.
Provide a quality score for each conversation, for example from 0 to 100.
Classify the risk level as Low, Medium, High, or Critical.
Include the specific sentence or message that triggered the alert.
Recommend the next action, such as contacting the customer, reopening the case, escalating the issue, or giving feedback to the advisor.
Example output:
Quality Score: 58 / 100
Risk Level: High
Customer Sentiment: Upset / dissatisfied
Summary:
The customer requested urgent support and expressed frustration due to a delayed response. The advisor provided a partial answer but did not confirm a clear solution or next step.
Detected Issues:
Customer expressed frustration.
No clear resolution was provided.
The conversation ended without confirming customer satisfaction.
There is a possible risk of losing customer trust.
Evidence:
“I have been waiting all morning and nobody has solved this for me.”
Advisor Evaluation:
The advisor maintained a formal tone but did not show enough empathy or provide a concrete solution. The next step, responsible person, and estimated response time were not clearly communicated.
Recommended Action:
Contact the customer immediately, apologize for the delay, confirm the current status of the case, and assign follow-up to a supervisor.
Priority: High
Business value:
This feature would help improve service quality, reduce customer complaints, recover dissatisfied customers, identify training opportunities for advisors, and give supervisors a more efficient way to focus on the conversations that need urgent attention.
N
Nabilah Binti Salleh
Merged in a post:
AI: Automated conversation quality review and reporting
Wbsandy Kanzouh
Business Problem
Our medical consultation team needs a structured way to evaluate the quality of conversations between specialists and customers, particularly to support training and quality assurance for newly onboarded team members.
Currently, managers must manually review conversations to assess tone, clarity, professionalism, and whether the customer’s needs were properly understood. This process is time-consuming and difficult to scale.
Desired Outcome
Enable an AI-powered quality assurance feature that can:
- Automatically review conversations after they are completed
- Evaluate communication quality (tone, clarity, professionalism, understanding of customer needs)
- Generate a structured summary or quality score
- Add review comments or notes at the end of the conversation
- Export the evaluation results to Google Sheets (or similar) for manager review and tracking
N
Nabilah Binti Salleh
Hi Wbsandy Kanzouh- thank you for your request.
To further understand it, could you share with us the exact use case/examples that you are looking to solve? That would help us picture the opportunity better.
Wbsandy Kanzouh
Hi Nabilah Binti Salleh , sure!
We have a medical consultation team, and we are currently in need of an AI agent that can review the quality of conversations between our specialists and customers after each consultation is completed.
The purpose of this review is to ensure that our team is fully understanding the customer’s needs and communicating in an effective and professional manner, especially when it comes to newly trained team members.
Therefore, we require a Quality Assurance function that can provide structured feedback and add notes at the end of each conversation, which can then be reviewed and by the managers.