AI support quality research toolkit
Everything you need to evaluate AI-powered customer support, uncover quality gaps, and improve the customer experience.
Download ready-to-use templates to assess AI agent responses, identify recurring failures, and prioritize improvements based on real customer interactions.
Ideal for support and CX teams evaluating AI chatbots, virtual agents, and human-assisted support workflows.
The 25–50 interactions represent a starter audit, not a statistically representative sample or substitute for continuous QA.
A practical framework for assessing response accuracy, resolution quality, policy compliance, escalation, and customer experience. Includes a ready-to-use review checklist and scoring criteria.
Download templateScore individual support conversations, identify recurring failure patterns, compare quality across issue types, and prioritize the improvements that matter most.
Download templateReviewing 25–50 conversations is a great starting point. But support quality doesn't stop there. See how unitQ Support QA helps teams evaluate support interactions at scale and identify where customer experiences break down.
Explore unitQ Support QAWhat questions will this research answer?
By the end of this assessment, you’ll know:
Good-fit check
Is this toolkit right for you?
How it works
Research process
Collect real customer conversations
Gather a representative mix of resolved, escalated, abandoned, and repeat-contact interactions. Remove or mask sensitive customer information before analysis.
Define your quality standards
Establish evaluation criteria for accuracy, resolution, tone, compliance, and escalation.
Evaluate AI responses
Score conversations against your quality standards and document evidence of successful and unsuccessful outcomes.
Identify recurring quality gaps
Analyze common failure modes, affected customer journeys, and differences across issue types.
Prioritize improvements
Determine what to fix in knowledge sources, agent instructions, escalation logic, and QA workflows.
What you'll uncover
The problems a starter audit surfaces
Scored conversations roll up into a prioritized list of quality gaps, each with a severity, a recommended fix, and an owner.
Illustrative examples. The workbook ships them as rows EX-001 to EX-007; delete them before you start.
Keep exploring
Related resources
How to Measure AI Agent and Chatbot Conversation Quality: A Practical Framework
A practical framework for measuring AI response quality, customer outcomes, and escalation performance.
Read the guideHow to audit AI agent handoffs to human agents
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Read the guideHow to QA Support Tickets: A Step-by-Step Guide
Establish quality standards, evaluate conversations, and continuously improve AI and human support.
Read the guideHow to build support QA scorecards that agents trust
Build a scorecard that evaluates accuracy, resolution, compliance, and customer experience.
Read the guideIs AI making customer support worse?
Original research on how customer-facing teams evaluate AI support quality and customer outcomes.
Read the findingsunitQ Support QA
Evaluate customer support conversations, uncover quality issues, and identify opportunities to improve AI and human support.
Explore unitQ Support QABeyond the sample
You can audit 50 conversations by hand. You can’t audit 50,000.
Sampling shows you which failures exist, but not how often they happen or who they reach. unitQ Support QA scores every conversation you connect against your own rubric, so recurring issues surface with the evidence behind them, for AI agents and human agents alike.