QA scorecards define the criteria that Reddy’s AI uses to automatically evaluate and grade calls. Each QA item consists of a title, type, requirement description, point value, and status indicator. These elements work together to power Reddy’s automatic grading and agent feedback.
Need step-by-step setup instructions? See the QA Setup Guide for detailed configuration steps.
Scoring: Numerical grade based on performance quality
The requirement description is where you define what the AI should evaluate. Reddy transforms your written requirements into automatic grading logic.
Writing Effective Requirements
Be specific about expected behaviors, phrases, or actions
Include context about when the requirement applies
Define success criteria clearly
Use examples to illustrate compliance
Example Requirements:
Greeting
Verification
Empathy
Weak: “Agent greets customer”Strong: “Agent greets customer with: company name, their own first name, and offers assistance. Example: ‘Thank you for calling [Company], this is [Name], how can I help you today?’”
Weak: “Agent verifies account”Strong: “Agent verifies the customer’s identity by collecting at least two of the following: full name, phone number, email address, or account number before discussing account details.”
Weak: “Agent shows empathy”Strong: “Agent acknowledges customer frustration or concern with empathetic language such as ‘I understand how frustrating that must be’ or ‘I can see why you’d be concerned about this’ before moving to resolution.”
Point values are weighted relative to all other items in the scorecard. Higher point values increase an item’s impact on the overall compliance score.
Align point values with your organization’s priorities. If account verification is critical, assign it more points than closing pleasantries.
Green Status: QA item is active and being automatically graded by Reddy’s AIYellow Status: QA item needs AI model updates before automatic grading activates. Contact your Reddy team if items remain yellow for extended periods.
Group related QA items into sections that follow your call flow. This makes scorecards easier to navigate and ensures evaluations track the natural progression of customer interactions.Example:
After configuring your QA scorecard, test it against real customer calls to ensure Reddy’s grading accuracy matches your quality standards.Start with 20 manually scored calls to establish a solid benchmark dataset. Focus on calls representing typical scenarios.
Before evaluating QA scorecard accuracy, upload real customer calls to test your scorecard against actual interactions. Use these calls to verify that Reddy’s AI grades match your quality standards and to identify any scoring logic that needs refinement.Upload Call Recordings
