---
title: "AI Quality Assessment Overview"
description: "Learn how AI QA in ConnectiveOne helps assess service quality in chats and calls."
---

# AI Quality Assessment Overview

High-level overview of AI-powered quality assessment (AI QA) in ConnectiveOne. This page explains what AI QA does, who it is for, and how it helps you control service quality — with a short video at the top and links to detailed instructions.

<video src="https://storage.static.kwizbot.io/connectiveone-docs/learn/ai-qa/AI_Quality_Assurance,_Simplified_.mp4" controls></video>

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## Who This Page Is For

- **Supervisors and quality managers** — monitor service quality, find weak points, and track improvements over time.
- **Analysts** — work with reports, dashboards, and exports for deeper analysis.
- **Team leads** — review individual conversations and coach operators based on AI assessments.

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## What AI QA Does

AI QA automatically analyzes dialogs and calls according to a configured checklist. Typical examples:

- Checks whether the greeting, tone, and closing of the dialog match your standards.
- Verifies whether mandatory steps were followed (verification, disclaimers, scripts).
- Highlights risk situations — rude replies, missed promises, sensitive topics.
- Assigns a score to each dialog according to your internal criteria.

You configure the checklist once, and AI QA helps apply it consistently across all dialogs — even when you have thousands of conversations per day.

This means AI QA helps you:

- apply the same quality standards to every conversation;
- quickly find conversations that need attention (low scores, risky topics);
- reduce manual sampling and scoring so the team can focus on coaching instead of spreadsheets.

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## How AI QA Fits Into Your Workflow

1. **Operators** communicate with customers in chats or calls.
2. **AI QA** analyzes selected dialogs according to your checklist.
3. **Supervisors and analysts** review the results in reports and dashboards.
4. **Team leads** use assessments to give feedback and plan training.

You can start with a small pilot (for one team or one line of business) and later scale AI QA to more queues and segments.

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## Next Steps: Detailed Guides

Use these guides to go from overview to concrete actions in the interface:

- **For supervisors and analysts**
  - [Analyst — Quality Assurance Scenarios Hub](/en/quality_assurance/analyst-hub.md)
  - [Supervisor hub for Quality Assurance](/en/quality_assurance/supervisor-hub.md)
- **For admins and configuration**
  - [Admin hub for Quality Assurance](/en/quality_assurance/admin-hub.md)
- **For day-to-day work with assessments**
  - [Assess a request with AI QA](/en/quality_assurance/how-to/assess-request.md)
  - [View assessments list](/en/quality_assurance/how-to/view-assessments-list.md)
  - [View assessment details](/en/quality_assurance/how-to/view-assessment-details.md)
  - [View quality reports](/en/quality_assurance/how-to/view-quality-reports.md)
  - [View quality dashboard](/en/quality_assurance/how-to/view-quality-dashboard.md)
  - [Filter assessments](/en/quality_assurance/how-to/filter-assessments.md)