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Traditional quality assurance (QA) processes are constrained by manual evaluation methods, which are resource-intensive and limited to a small sample of interactions. This results in insufficient coverage, scoring inconsistencies, and delayed feedback.

The Expertflow AI-Driven Quality Management module addresses these limitations by enabling automated, objective evaluation of customer interactions. The system provides two operational modes:

1. Full Automation: The AI evaluates 100% of closed interactions.

2. Hybrid Mode: Flexibility to assign a certain percentage to AI and human evaluators.

The AI engine processes interaction data (e.g., transcript, metadata) and automatically scores the interaction based on the configured evaluation form. This ensures consistent, scalable scoring and reduces the manual workload associated with traditional QA. It currently supports English and Arabic (Egyptian dialect).

Streamline quality evaluations across all interaction channels (voice, chat, email, etc.) within one unified system. The Multi‑Channel Support allows consistent performance assessments across all touchpoints.

Generate five key reports to track evaluation activity and compare performance across agents, teams, and evaluators:

• Review Volume

• Agent & Team Skills

• Team Comparison

• Reviewer Performance

• Quality League Table

These reports help identify performance trends, skill gaps, and evaluation consistency—supporting team calibration.

Access full conversation content, including both conversation activities and conversation data, in a single, structured interface. The Conversation View component enables Quality Managers and Evaluators to review assigned interactions, complete evaluations using the attached evaluation form, and check agent performance with context-rich data.

Note: Conversation view is channel-agnostic and supports all Expertflow CX channels. Currently, the WebChat, Voice (CISCO UCCE), and Email channels have passed QA.

Evaluators can play voice and screen recordings linked to agent–customer interactions directly within the Conversation View. The player supports basic playback functions, enabling evaluators to evaluate conversations and on-screen actions in one place.

Filter conversations using agent, team, date/time, wrap‑up code, direction and sentiment via the Conversation List. This enables Quality Managers to quickly identify relevant conversations and focus evaluation efforts where they’re most needed.

Create structured, multi-section evaluation forms tailored to your quality assurance needs. The Form Builder supports a wide range of question types—including dropdowns, multiple choice, rating scales, and text inputs—along with configurable weight assignments at form, section, and question level. Built-in checks ensure that total weights are correctly distributed and sum to 100% where required.

The Review Scheduler enables Quality Managers to automate one-time or recurring evaluations using configurable rules. Schedules can be created based on agent groups, interaction metadata (such as call duration, wrap-up codes, or direction), and linked evaluation forms. Managers can assign reviewers, define deadlines, and configure automated reminders to ensure timely completion. Recurring schedules automatically assign new conversations that meet selected criteria—streamlining the QA process and reducing manual workload.

The Review Screen is a centralized workspace for viewing, accessing, and managing evaluations. It supports both Quality Managers and Evaluators with role-based access to relevant evaluator and allows users to filter, track, and initiate evaluations directly from one place.

Define the performance threshold that triggers alerts for low-scoring evaluations. This ensures that Quality Managers are promptly notified of conversations requiring immediate attention. The business can manage low-scoring evaluation alerts, sent to Quality Managers, either in real-time for individual reviews or as bulk summaries at set intervals. All alert settings can be managed through a dedicated configuration tab, offering flexibility and control over notification preferences.

Introduces a custom Python-based ETL (Extract, Transform, Load) tool enabling users to extract data from various sources, perform transformations, and load the results into different targets. This is designed for easy deployment, customization, and orchestration, making it ideal for scalable data workflows.