Introduction to AutoQA

Ensuring high-quality interactions is essential to delivering exceptional customer experiences. The Quality module in Quack AI automates support evaluations, tracks performance trends, and helps your team continuously improve through data-driven insights.

Purpose of QA

Quality Assurance (QA) ensures every interaction aligns with your organization’s standards for accuracy, empathy, and efficiency.

QA in Quack AI helps you:

  • Ensure every customer interaction meets your quality benchmarks

  • Improve consistency and accuracy across all support agents

  • Identify coaching opportunities and close process gaps

💡 Pro Tip: Use QA results as a coaching tool, not just a performance metric, to foster continuous learning.

Tracking your AutoQA Metrics

Challenges with Traditional QA

Traditional QA methods often fall short due to:

  • Manual reviews that are time-consuming and subjective

  • Low ticket coverage that limits visibility into overall performance

Quack AI eliminates these challenges with AutoQA, providing real-time, scalable quality assurance across every interaction.

Automated QA Scoring

Auto QA in Quack AI automatically generates Quality Scores based on predefined metrics set by your organization. These scores evaluate how well both AI and human agents handle customer inquiries. Common QA Metrics include:

  • Communication: Did the agent listen and respond clearly?

  • Product Knowledge: Was the response accurate and informative?

  • Problem Solving: Did the agent resolve the issue efficiently?

  • Empathy & Tone: Did the agent acknowledge the customer’s concerns appropriately?

The AutoQA Process

The AutoQA process includes four key components: Scorecards, Evaluations, Validations, and Reporting & Feedback.

Scorecards

Scorecards define the rules and metrics that Quack AI uses to evaluate support tickets.

You can:

  • Create custom scorecards: Design flexible templates to auto-evaluate each interaction

  • Add briefs: Provide clear guidance so the AI interprets questions the same way your QA team does

  • Use multiple scorecards: Tailor scorecards by channel, product, or team to ensure relevance

💡 Pro Tip: Add detailed briefs to improve AI grading accuracy and alignment with your internal QA standards.

Evaluations

AutoQA automatically evaluates 100% of closed tickets - for both human and AI agents.

Features include:

  • Team and Agent View: Drill down by team or agent to review performance trends

  • Scorecard View: See per-agent and overall ticket results

  • Manual Adjustments: Correct scores directly, Quack AI learns from each change

  • Searchable Evaluations: Access any ticket evaluation in Explore

💡 Pro Tip: Manual adjustments help Quack learn how your team interprets context, improving future accuracy.

Validations

Validation Sets allow you to manually review selected tickets to fine-tune AI grading logic.
They help refine AutoQA’s precision and ensure consistent, high-confidence scoring.

You can create:

  • Custom validation sets by topic, sentiment, or ticket volume

  • Targeted validation sessions for specific problem areas (e.g., long TTR, low CSAT, or complex cases)

💡 Pro Tip: Run 100 validations after creating or updating a scorecard, then 50 weekly to maintain alignment.

Reporting & Agent Feedback

Access AutoQA reports to analyze performance and deliver targeted coaching.

Reports provide:

  • Multi-level scoring: Team-level and agent-level breakdowns

  • Shareable summaries: Export agent reports highlighting top and bottom performers

Use QA insights to drive 1:1 coaching, ticket-based feedback, and progress tracking over time.

AutoQA Best Practices

To maximize accuracy and performance:

  1. Review team performance weekly using reports

  2. Complete 100 validations when a new scorecard is created

  3. Continue with 50 validations weekly for ongoing accuracy

  4. Identify performance trends and create targeted improvement plans

  5. Schedule recurring coaching sessions using QA data

💡 Pro Tip: Treat AutoQA as a living system - frequent calibrations and feedback make your AI smarter and your agents stronger.

Getting Started

  1. Define your QA objectives (e.g., accuracy, tone, compliance)

  2. Assign a QA Project Owner in Quack AI

  3. Create your custom Scorecard(s)

  4. Build a custom Validation Set

  5. Run 100 initial validations to fine-tune AI logic

  6. Conduct weekly QA checks across ~50 tickets

  7. Refine scoring briefs and prompts to improve precision

  8. Schedule ongoing agent coaching based on insights